Comparison of the accuracy of 9 intraocular lens power calculation formulas after SMILE in Chinese myopic eyes

Comparison of the accuracy of 9 intraocular lens power calculation formulas after SMILE in Chinese myopic eyes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparison of the accuracy of 9 intraocular lens power calculation formulas after SMILE in Chinese myopic eyes Liangpin Li, Liyun Yuan, Kun Yang, Yanan Wu, Simayilijiang Alafati, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3080000/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Nov, 2023 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract As of 2021, over 2.8 million cases of small incision lenticule extraction (SMILE) procedures had been performed in China. However, there remains limited knowledge regarding the selection of intraocular lens (IOL) power calculation formulas for post-SMILE cataract patients. This study included 52 eyes of 26 myopic patients from northern China who underwent SMILE at Tianjin Eye Hospital from September 2022 to February 2023 and was designed to investigate the performance of multiple IOL calculation formulas in post-SMILE patients using a theoretical surgical model. We compared the postoperative results obtained from three artificial intelligence (AI)-based formulas and six conventional formulas provided by the American Society of Cataract and Refractive Surgery (ASCRS). These formulas were applied to calculate IOL power using both total keratometry (TK) and keratometry (K) values, and the results were compared to the preoperative results obtained from the Barrett Universal II (BUII) formula in SMILE cases. Among the evaluated formulas, the results obtained from Emmetropia Verifying Optical 2.0 Formula with TK (EVO-TK) (0.40 ± 0.29 D, range 0 to 1.23 D), Barrett True K with K (BTK-K, 0.41 ± 0.26 D, range 0.01 to 1.19 D), and Masket with K (Masket-K, 0.44 ± 0.33 D, range 0.02 to 1.39 D) demonstrated the closest proximity to BUII. Notably, the highest proportion of prediction errors within 0.5 D was observed with BTK-K (71.15%), EVO-TK (69.23%), and Masket-K (67.31%), with BTK-K showing a significantly higher proportion compared to Masket-K (p < 0.001). Our research indicates that in post-SMILE patients, EVO-TK, BTK-K, and Masket-K may yield more accurate calculation results. At the current stage, AI-based formulas do not demonstrate significant advantages over conventional formulas. However, the application of historical data can enhance the performance of these formulas. Health sciences/Medical research Health sciences/Medical research/Study design IOL power calculation formula SMILE refractive error keratometry total keratometry Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Small incision lenticule extraction (SMILE) is the latest generation of refractive surgery and is widely utilized for treating refractive errors. SMILE involves creating a corneal lenticule within the stromal layer, which is subsequently removed through a small incision. This innovative technique eliminates the need for corneal flap preparation while ensuring enhanced precision and superior biomechanical outcomes compared to alternative methods 1 . On the other hand, cataracts, as one of the most common causes of blindness worldwide 2 , 3 , are very common in the elderly population, and studies have shown that the prevalence of cataracts in the population over 80 years old is as high as 60–90% 4,5 . In the future, we can envision a substantial number of cataract patients who have previously undergone SMILE surgery. However, this presents a dilemma for surgeons. While these individuals hold high expectations for optimal postoperative visual quality, the corneal morphology undergoes changes because of the previous refractive surgery. Consequently, the accuracy and predictability of calculating the diopter for intraocular lens (IOL) are significantly reduced. What is even more challenging is the inability to rectify any refractive complications that may arise after the operation through additional laser surgery as keratectomy has already been performed. As one of the most important guarantees for the accurate calculation of IOL power, the IOL formula plays an important role. To be more suitable for different eye conditions, the IOL formula is constantly innovated. The conventional formula predicts the effective lens position (ELP) by corneal curvature 6 , but the corneal curvature of the eyes after refractive surgery changes greatly, resulting in a large error 7 . As a new method of refractive surgery, we cannot directly apply the data of previous Laser-assisted In Situ Keratomileusis (LASIK), so there is an urgent need to find accurate formulas for cataract patients with a history of SMILE. However, due to the short history of SMILE surgery and because most of the patients are young, a large enough number of cataract patients who have undergone SMILE surgery is unavailable, making it difficult to find the best formula directly. To solve this problem, the theoretical surgical model proposed by Lazaridis, A., et al. is the most reliable method in this kind of research at present 8 , 9 . The theoretical surgery involves optical biometry measurements before and after SMILE surgery, using the same formula to calculate and compare the pre- and postoperative measurements to evaluate the stability of various formulas, making it an ideal research method in the current situation. In this study, we improved upon this theoretical surgical model by using the preoperative result of the Barrett Universal II (BUII) formula as a baseline. BUII is one of the fifth generation IOL calculation formulas known for its high accuracy and stability in the target population, particularly in patients with medium to long axial lengths (25.63 ± 0.81 mm in our study) 10 – 14 . If the calculation accuracy in post-SMILE patients can approach the performance of BUII in patients without a history of refractive surgery, it would undoubtedly be a considerable success. Therefore, we used BUII for preoperative IOL power calculation, and the postoperative formula with results closest to BUII was considered more accurate. At present, the emergence of artificial intelligence (AI) calculation formulas based on big data has led to the development of all kinds of formulas, including the Kane, Emmetropia Verifying Optical 2.0 (EVO) and Pearl-DGS formulas 15 – 17 . The most important feature of this type of formula is its self-learning ability, and it is expected to make a breakthrough in the field of accurate computing after refractive surgery. In addition, some formulas, such as the Barrett True K (BTK), Haigis-L and Masket formulas, have been proven to be accurate after LASIK. Therefore, the purpose of this study is to explore the suitability of these formulas for patients after SMILE through the theoretical surgical model. METHOD This research project was approved by the Ethics Committee of Tianjin Eye Hospital and was conducted according to the principles of the Helsinki Declaration. The study included 52 eyes of 26 patients with myopia who received refractive treatment at Tianjin Eye Hospital from October 2022 to March 2023. Each patient was informed of the research content and signed the informed consent form before the surgery. All patients were evaluated by ophthalmology before the operation, including uncorrected distance visual acuity and corrected distance visual acuity, manifest refraction, and cycloplegic refraction, slit lamp examination, optical biometrics with IOL Master700 (Carl Zeiss Meditec AG, Jena, Germany) and corneal tomography with Pentacam HR (OCULUSO ptikgerate: GmbH). Postoperative evaluation was performed at least 3 months after surgery, including uncorrected distance visual acuity and corrected distance visual acuity measurements, manifest refraction and IOLMaster700 measurements. The inclusion criteria included age more than 18 years, stable refraction 2 years before operation, corrected distance visual acuity of 20/20 or better, suspended use of soft contact lenses for more than 2 weeks, and suspended use of hard contact lenses for more than 4 weeks. The exclusion criteria were slit lamp examination for any corneal or lens opacity or pathological changes, previous corneal surgery, ocular trauma or intraocular surgery, severe dry eye, glaucoma, corneal disease or ocular infection, keratoconus or suspected keratoconus, remaining stromal expected < 280 µm, posterior scleral staphyloma and so on. All operations were performed by the same experienced surgeon (Wang Y) using the VisuMax femtosecond laser (Carl Zeiss Meditec AG, Jena, Germany). There were no intraoperative or postoperative complications. Since the lens and the posterior segment of the eye are almost unchanged except for the change in the cornea caused by SMILE procedure, the change in IOL power before and after the operation should theoretically be equal to the change in the corneal refractive power caused by the operation. In this study, a theoretical surgical approach simulating lens removal was adopted. Preoperative and postoperative optical biometry measurements were performed using the IOL Master 700. Preoperatively, we used BUII formula to calculate IOL power (the target refraction was selected as the equivalent spherical diopter of the patient before SMILE). Postoperatively, three different AI-based formulas, namely, Kane ( www.iolformula.com ), EVO ( www.evoiolcalculator.com/calculator.aspx ), and Pearl-DGS (iolsolver.com/complex) as well as six commonly used formulas (both available at ASCRS online, https://ascrs.org/tools ), namely, BTK, Barrett True K no history (BTKNH), Masket, modified-Masket (m-Masket), Shammas, and Haigis-L formulas, were used to calculate the IOL power corresponding to a target refractive power of 0 D. All calculation results were rounded to two decimal places. Each formula's calculation result minus the preoperative BUII calculation result yielded the prediction error (PE), its absolute value represented the absolute prediction error (AE), and the median of AE was abbreviated as MedAE. During calculations, the keratometry (K) and total keratometry (TK) values were input into each formula separately, denoted as Kane-K/Kane-TK, BTK-K/BTK-TK, and so on. To maintain consistency among the formulas in IOL power calculations, the same model of IOL (AR40e, Johnson & Johnson Vision Care, Inc., A constant = 118.71; A1 = 1.242, A2 = 0.2, A3 = 0.1) was chosen. Descriptive statistics (i.e., mean, SD, and range) were performed using Microsoft Excel 2016 (Microsoft Corp.). Statistical analysis was conducted using SPSS 26.0 software (IBM Corp.; Armonk, NY, USA). The Kolmogorov‒Smirnov test was used to assess the distribution normality of the results of each calculation method, PE, and AE. The results were expressed as the mean ± standard deviation, and a one-sample t test was used to compare the differences in PE between various calculation methods and zero. One-way analysis of variance (ANOVA) was used to compare the differences in PE and AE among the three calculation methods. In cases where a significant overall difference was observed, pairwise comparisons between the AE in any two groups were conducted using one-sample t test or Friedman test. The Chi-square test was employed to compare the differences in the percentage of eyes falling within ± 0.5 D and ± 1.00 D of AE among the formulas. A p value of less than 0.05 was considered statistically significant. RESULT This study included a total of 26 patients (52 eyes, 11 males and 15 females) who received treatment at Tianjin Eye Hospital in Tianjin, China, from September 2022 to February 2023. The mean follow-up period after SMILE surgery was 4 ± 2 months (ranging from 3 to 8 months). The age of the patients ranged from 18 to 35 years, with a mean age of 26.36 ± 6.15 years. The preoperative average central corneal thickness (CCT) was 546.3 ± 31.57 µm (ranging from 494 to 620 µm), while the postoperative average CCT was 475.3 ± 40.02 µm (ranging from 413 to 586 µm). The preoperative average spherical equivalent (SE) was − 4.75 ± 1.36 diopters (ranging from − 1.75 to -8.25 D), and the postoperative average SE was − 0.53 ± 0.32 D (+ 0.125 D to -1.50 D). The preoperative average axial length (AL) was 25.63 ± 0.81 mm (ranging from 23.97 to 27.53 mm), and the postoperative average AL was 25.52 ± 0.81 mm (ranging from 23.86 to 27.44 mm). The preoperative average ACD was 3.81 ± 0.312 mm, and the postoperative average ACD was 3.70 ± 0.183 mm. The average reduction in ACD after surgery was 0.114 ± 0.07 mm. All patients underwent SMILE surgery for myopia correction, and their postoperative visual acuity was 20/20 or better. Additional patient characteristics and biometric data are summarized in Table 1 . Table 1 Patient demographic and ocular biometry data before and after SMILE Eyes/patients 52/26 Sex M/F, 11/15 Age (years) 26.19 (18, 25) AL (mm) Preop 25.63 ± 0.81, (23.97, 27.53) Postop 25.52 ± 0.81, (23.86, 27.44) ACD (mm) Preop 3.81 ± 0.31 Postop 3.70 ± 0.28 Keratometry (D) K TK Preop 43.36 ± 1.29 43.37 ± 1.30 Postop 39.58 ± 1.47 38.85 ± 2.00 SE (D) Preop -4.75 Postop -0.53 CCT (µm) Preop 548.11 ± 32.28 Postop 475.25 ± 40.02 M = Male; F = Female; AL = Axial length; ACD = Anterior chamber depth; Preop = preoperative; Postop = postoperative; SE = spherical equivalent; CCT = central corneal thickness The Kolmogorov‒Smirnov test showed that the calculation results and PE of each formula were normally distributed. Using one-way ANOVA to analyse the variance of 18 different data points, it was found that there was a significant difference in PE among the groups (p < 0.001). Among the AEs of each group, Kane-K, Kane-TK, EVO-TK, m-Masket-TK, BTK-K, BTK-TK, BTKNH-TK, Shammas, Shammas-TK, Haigis-L-K, and Haigis-L-TK were normally distributed, while the AEs of the other formula groups did not follow a normal distribution. The one-sample t test comparing PE with zero showed that, except for EVO-TK (p = 0.951), Masket-TK (p = 0.885), BTK-TK (p = 0.059), and BTKNH-TK (p = 0.625), which represent good consistency with the results of BUII, all other formulas were significantly different from zero (all p < 0.001). The formulas with the smallest PE were Kane-K (-1.373 ± 0.78 D), Kane-TK (-0.64 ± 0.61 D), and EVO-K (-0.53 ± 0.50 D), indicating that these formulas yielded results smaller than the preoperative BUII results, which could result in hyperopic shift. The formulas with the largest PE were Shammas-TK (1.58 ± 0.68 D), BTK-TK (1.46 ± 0.71 D), and m-Masket-TK (0.96 ± 0.54 D), suggesting a tendency for myopic shift postoperatively (Fig. 1 a). The formulas with an average AE of less than 0.5 D were EVO-TK (0.40 ± 0.29, range 0 to 1.23, MedAE = 0.36 D), BTK-K (0.41 ± 0.26, range 0.01 to 1.19, MedAE = 0.35), and Masket-K (0.44 ± 0.33, range 0.02 to 1.39, MedAE = 0.37) (Fig. 1 b). The Friedman test and paired t test showed no significant differences among the three formulas (EVO-TK: BTK-K, p = 0.057; EVO-TK: Masket-K, p = 0.941; BTK-K: Masket-K, p = 0.905), indicating that these three formulas yielded results closest to the preoperative BUII results. Figure 3 presents a more detailed frequency distribution of the PE in these three formulas. Among them, Masket-K demonstrates the highest distribution between − 0.25 D and 0.25 D, with a total of 20 eyes. EVO-TK shows a uniform distribution on both sides of 0D. On the other hand, BTK-K is predominantly distributed between − 0.75 D and − 0.25 D, indicating slightly lower results compared to BUII, which may result in mild hyperopic shift. The formulas with an average AE exceeding 1 D were Shammas-TK (1.584 ± 0.68, range 0.33 to 3.56, MedAE = 1.61), Haigis-L-TK (1.464 ± 0.71, range 0.2 to 3.58, MedAE = 1.41), and Kane-K (1.373 ± 0.78, range 0.08 to 3.03, MedAE = 1.34). The AE for these three formulas followed a normal distribution, and pairwise t tests showed significant differences among all three pairs (p < 0.001, Fig. 1 b). More detailed information can be found in Table 2 . Table 2 Refractive prediction errors from BUII preoperative of 52 eyes. Formula Mean SD min max median EVO-TK 0.40 0.29 0.00 1.23 0.36 BTK-K 0.41 0.28 0.01 1.19 0.35 Masket-K 0.44 0.33 0.02 1.39 0.37 m-Masket-K 0.53 0.38 0.01 1.59 0.42 BTK-TK 0.55 0.40 0.02 1.57 0.52 BTKNH-K 0.56 0.38 0.01 1.42 0.45 EVO-K 0.61 0.39 0.01 1.41 0.54 MASKET-TK 0.64 0.46 0.03 1.86 0.51 Pearl-DGS-TK 0.64 0.59 0.04 2.73 0.47 Pearl-DGS-K 0.70 0.47 0.02 1.71 0.59 Kane-TK 0.72 0.50 0.01 1.96 0.67 Haigis-L-K 0.78 0.56 0.01 2.35 0.73 BTKNH-TK 0.81 0.55 0.04 2.26 0.73 Shammas-K 0.83 0.65 0.00 2.51 0.83 m-Masket-TK 0.96 0.54 0.06 2.28 0.90 Kane-K 1.37 0.78 0.08 3.03 1.34 Haigis-L-TK 1.46 0.71 0.20 3.58 1.41 Shammas-TK 1.58 0.68 0.33 3.56 1.61 In addition, when using TK values for calculations, the Friedman test and paired t tests showed that the inclusion of TK was superior to the inclusion of K values in the three artificial intelligence formulas. There were significant differences between the Kane and EVO formulas, while no significant difference was found in the Pearl-DGS formula (Kane: p < 0.001, EVO: p = 0.004, Pearl-DGS: p = 0.218). The remaining six formulas showed the opposite trend, except for the BTK formula, where the accuracy significantly decreased when TK values were used (Masket p < 0.001, m-Masket p < 0.001, BTK p = 0.587, BTKNH p = 0.03, Shammas p < 0.001, Haigis-L p < 0.001). The distribution of AE within different ranges ( 2D) is shown in Table 3 , and Fig. 2 is the stacked histogram of these formulas. Among them, the formulas with the highest proportion of AE < 0.5D were BTK-K (71.15%), EVO-TK (69.23%), and Masket-K (67.31%). And a more detailed frequency distribution of the EVO-TK, BTK-K, and Masket-K formulas is shown in Fig. 3 . The Chi-square test showed that the proportion of AE within 0.5 D for the BTK-K formula was significantly higher than that for the Masket-K formula (χ 2 = 22.845, p < 0.001). There were no significant differences between BTK-K and EVO-TK or between EVO-TK and Masket-K (χ 2 = 1.278, p = 0.258; x2 = 0.93, p = 0.76, respectively). The formulas with the highest proportion of absolute mean PE within 1.0 D were BTK-K (98.08%), Masket-K (96.15%), and EVO-TK (94.23%). The Chi-square test showed no significant differences among the three formulas (BTK-K and Masket-K: χ 2 = 0.41, p = 0.84; BTK-K and EVO-K: χ 2 = 0.62, p = 0.803; Masket-K and EVO-TK: χ 2 = 1.27, p = 0.721). The formulas with a PE greater than 2 D were Pearl-DGS-TK (3.84%), Haigis-L-K (3.84%), m-Masket-TK (3.84%), Haigis-L-TK (17.30%), and Shammas-TK (21.15%), indicating that using these formulas may result in larger errors. Table 3 The percentage of eyes falling within different ranges of AE among the formulas Formula ± 0-0.5 D(%) ± 0.5-1.0 D(%) ± 1.0-1.5 D(%) ± 1.5-2.0 D(%) >±2.0 D(%) BTK-K 71.15 26.92 1.92 0.00 0.00 EVO-TK 69.23 25.00 5.77 0.00 0.00 Masket-K 63.46 32.69 3.85 0.00 0.00 BTKNH-K 57.69 26.92 15.38 0.00 0.00 m-Masket-K 53.85 32.69 11.54 1.92 0.00 Pearl-DGS-TK 51.92 25.00 15.38 3.85 3.85 Masket-TK 50.00 30.77 11.54 7.69 0.00 EVO-K 48.08 32.69 19.23 0.00 0.00 BTK-TK 46.15 40.38 11.54 1.92 0.00 Haigis-L-K 42.31 26.92 21.15 5.77 3.85 Pearl-DGS-K 42.31 25.00 32.69 0.00 0.00 Kane-K 42.31 32.69 13.46 11.54 0.00 Shammas-K 38.46 23.08 23.08 15.38 0.00 BTK-NO-TK 32.69 34.62 28.85 3.85 0.00 m-Masket-TK 21.15 38.46 25.00 11.54 3.85 Kane-TK 11.54 26.92 19.23 42.31 0.00 Haigis-L-TK 7.69 19.23 25.00 30.77 17.31 Shammas-TK 5.77 15.38 23.08% 34.62 21.15 DISCUSSION Although SMILE has become one of the mainstream refractive surgeries, there is limited knowledge about accurate IOL power calculation for cataract patients who have undergone SMILE surgery. Currently, most formulas for postrefractive surgery cases are based on LASIK or Photo Refractive Keratectomy procedures 18 – 21 . Even commonly used measurement devices hardly have options for post-SMILE measurements. Since its commercial application in 2011, millions of patients with refractive errors have undergone SMILE to eliminate the need for glasses. As time goes by, the significant number of patients has made it crucial to address the issue of accurate IOL power calculation for this patient group when they develop cataracts. The LASIK procedures involves creating a corneal flap using the femtosecond laser and then using the excimer laser to reshape the cornea, with an incision up to 20 mm. On the other hand, SMILE is entirely performed using the femtosecond laser and involves a small incision of only 2 mm 22 . Additionally, when correcting the refractive error of equal degrees, the excised corneal stroma during SMILE is generally thicker than that in LASIK. Due to these differences, we cannot directly apply the research experience from LASIK to SMILE patients. In our study, we found that the BTK-K, Masket-K, and EVO-TK formulas closely approximated the preoperative results obtained with BUII, with all three formulas having an average AE of less than 0.5D. The BTK formula includes a postrefractive surgery mode, while the Masket formula is specifically designed for refractive surgery. BTK, developed based on BUII, has demonstrated good performance in various retrospective consecutive case series studies. Ferguson et al. reported that BTK outperformed other ASCRS formulas in both myopic and hyperopic post laser refractive surgery patients (96 post myopic eyes and 47 post hyperopic eyes) 23 . Abulafia et al. found that BTK had significantly smaller prediction errors and a higher percentage of eyes within ± 0.50 D than the Shammas and Haigis-L formulas in postmyopic LASIK cases (80 postmyopic eyes) 24 . Notably, Savini et al. found that BTK yielded the best results when combined with measured posterior corneal power (PK, obtained with Pentacam) in cases with available historical data, while using PK without historical data led to larger errors (50 postmyopic eyes) 25 . Similarly, the Masket formula has been widely proven for its accuracy in post-LASIK patients, with several studies showing similar performance to the BTK formula 26 – 28 . Wu et al.'s meta-analysis demonstrated that the Masket formula was more accurate than the Haigis formula in postrefractive surgery patients 29 . Savini et al.'s prospective study found that the Masket formula may be the most reliable in postrefractive surgery cases lacking preoperative keratometry data but with known refractive change 27 . Among the three formulas, EVO-TK, the only AI-based formula supported by a large dataset, also yielded satisfactory results. Consistent with this, previous studies have demonstrated excellent performance of the EVO formula in cases with various corneal abnormalities. Ferrara et al. found that the EVO formula showed higher accuracy in post-RK cataract patients than the SRK/T and BTK formulas in a study involving 27 patients 30 . In another retrospective study of 110 patients with congenital cataracts, Lin et al. found that the EVO formula performed best in children, especially those under 24 months of age and with an axial length less than 21 mm 31 . As a new-generation formula utilizing big data and AI, the EVO formula exhibits excellent performance under various axial length conditions and can provide satisfactory results in complex and extreme ocular conditions 32 . The involvement of AI, especially deep learning, will help refine IOL formulas. The application of AI technology combined with big data in ophthalmology has become a trend. Miao et al. developed a model based on patient fundus photography data that surpassed the performance of resident doctors in determining ischaemia type and nonperfusion area and was comparable to senior doctors 33 . In a study by Farahat et al., two different AI systems were found to outperform resident doctors in diagnosing diabetic retinopathy. Wei et al. successfully applied AI to the screening of wet age-related macular degeneration by using deep learning on optical coherence tomography scan data from eight patients 32 . The time and resources of clinical doctors and researchers are limited, but once algorithms and AI models are established, AI can continuously learn and improve. Although the Kane formula based on AI and cloud computing has shown excellent performance in patients with long axial length and no history of refractive surgery 34 , 35 , it exhibited significant differences in this study compared to the expected results. We believe there are three possible reasons for this: 1) the formula lacks a specific postrefractive surgery module, preventing the use of historical data despite including CCT as a parameter; 2) the formula innovatively uses sex as a mandatory parameter but does not include WTW (white to white); 3) the development of the Kane formula was based on data from over 30,000 Caucasian individuals, while our study included only Northern Chinese individuals of East Asian descent. We hope that in the future, the Kane formula can incorporate more cases of East Asian individuals and include postrefractive surgery modules. The aforementioned three formulas utilize historical data and include preoperative SE and postoperative SE as parameters to correct the calculation results, highlighting the value of accurate historical data in calculations for postrefractive surgery patients. The Masket formula, specifically designed for refractive surgery, uses the following equation to correct the final result: IOLpost + (RC x 0.326) + 0.101 (where IOLpost is the calculated IOL power following refractive surgery and RC is the refractive change at the corneal plane) 36 . The specific algorithms for the EVO and BTK formulas have not been disclosed. Current mainstream formulas typically accommodate only preoperative and postoperative equivalent spherical powers, with PERAL-DGS being the one of the few formulas that incorporates preoperative corneal curvature radius as a calculation parameter. Preoperative corneal curvature can serve as a reference for calculating ELP and is an important parameter in the double-K method 6 . We believe that incorporating this parameter into the calculation formula will contribute to more accurate calculations, especially for patients with abnormal preoperative corneal curvature. There is still controversy regarding whether different corneal flap thicknesses affect the Corvis ST Biomechanical Index (CBI). Different corneal flap thicknesses may lead to differences in corneal CBI and stiffness parameters at first applanation 37 . Fang et al. found no influence of different flap thicknesses on CBI after SMILE, while another study showed significant differences in CBI and stiffness parameters at first applanation between 110 µm cap and 145 µm cap 38 . Lv et al. also found differences in CBI between 110 µm, 120 µm, and 130 µm caps 39 . Several studies have shown that the application of historical data improves calculation accuracy in patients after LASIK 25 , 40 , but the limitation lies in the difficulty of obtaining such data due to long intervals between cataract surgery and refractive surgery. It is not feasible to rely on every patient to keep their own medical records. We believe that storing standardized patient preoperative and postoperative examination data in an online database in the form of electronic data would provide the greatest benefit to patients 41 . In this study, we found that the Kane and EVO formulas performed better when using TK, while the traditional Masket, m-Masket, BTKNH, Haigis, and Shammas formulas should use K instead of TK. As a nonmeasured value, ELP is derived based on corneal curvature as the primary reference value. Theoretically, for the same IOL, the ELP remains constant. If the ELP prediction tends towards the cornea, the actual power obtained will be higher, leading to a myopic shift when the IOL is positioned in its actual location. Conversely, a hyperopic shift may occur. The accuracy of corneal curvature directly affects the predicted ELP. The IOL Master 700 calculates K using the traditional telecentric keratometry method, while TK is derived from swept-source OCT measurements of the posterior corneal surface combined with K values, rather than being a direct measurement. Theoretically, TK should be more accurate and closer to the actual corneal power. However, some literature suggests that simply incorporating TK values may not be suitable for all formulas 42 . In a clinical retrospective comparative study, Danjo et al. found that in cases of single-focus IOL implantation (225 eyes of 225 patients, IOL Master 700), K provided better accuracy than TK for various axial lengths in routine cataract patients when using formulas such as BUII, Haigis, SRK/T, Holladay 2, and Hoffer Q 43 . Chung et al. also found that in the selection of multifocal IOLs (543 eyes of 543 patients, IOL Master 700), K should be used instead of TK with formulas such as BUII, Haigis, SRK/T, and Holladay 2 44 . It appears that TK does not provide an advantage in routine cataract patients. However, in the selection of toric IOLs, combining TK with the Barrett toric formula can reduce the error in predicted residual astigmatism (247 eyes of 180 patients, IOL Master 700) 45 . Additionally, Yeo et al. found in a study of postrefractive surgery patients (64 eyes of 49 patients, IOL Master 700) that formulas such as BTK, Haigis-L, Shammas-PL, EVO, Hoffer Q, Holladay I, and SRK/T yielded better results when TK was used 46 . Similarly, studies have demonstrated that TK is superior to K in cases with complex corneal conditions such as combined corneal endothelial diseases and keratoconus 47 , 48 . Therefore, in designing this study, we used BUII with K preoperatively and compared TK and K postoperatively. We found that except for the EVO and Kane formulas, which performed better when using TK, the remaining formulas had larger errors. Further research is needed to determine whether TK should be used in postrefractive surgery patients. In the process of IOL power calculation, the corneal refractive power and axial length are fixed, and even in postrefractive surgery patients, the IOL Master 700 can accurately measure these two parameters. In our study, we selected measurement data from patients at least 3 months postoperatively because the corneal shape stabilizes after 3 months following SMILE surgery, with minimal changes over a certain period 49 . Xia et al. demonstrated the long-term stability and efficacy of SMILE surgery through a 10-year follow-up of patients who underwent the procedure 1 . Furthermore, since the K values for the most challenging ELP prediction are historical data that do not change, these formulas may still be applicable even if future changes in corneal shape occur. However, importantly, any formula can have deviations. This is where the advantage of comparing multiple formulas lies: obtaining similar results from different formulas provides us with confidence in selecting the IOL power, reducing the rate of significant refractive surprises. Although AI-based methods relying on big data require substantial data support from successful cases, they have not yet surpassed traditional formulas at this stage. However, with continuous deep learning, we believe that AI formulas will make significant progress. Additionally, we look forwards to AI systems that can assist us in selecting most reasonable results when different formulas yield disparate outcomes. CONCLUSION In this study, we aimed to investigate the predictive accuracy of various formulas for IOL power calculation in post-SMILE patients. The results revealed that the BTK-K, Masket-K, and EVO-TK formulas showed the closest agreement with the BUII baseline results, with AE values below 0.5 D. Further research is needed to validate the efficacy of these formulas in a larger population. The utilization of AI and deep learning in ophthalmology is a growing trend, and it has the potential to refine IOL formulas. It is essential to establish standardized electronic databases for storing patients' preoperative data to facilitate accurate calculations and improve patient outcomes. Future studies should continue to explore the role of AI models and refine the selection of IOL power calculation formulas in post-SMILE patients. Declarations FUNDING This research was funded by the Natural Science Foundation of Tianjin City, China (No. 21JCZDJC01250). The authors sincerely treasured the comments and suggestions from reviewers. ACKNOWLEDGEMENTS The authors thank all participants of this study for sharing their time and experiences. DATA AVAILABILITY All data generated or analyzed during this study are included in this published article and its supplementary information files. References Xia, F., Chen, Z., Miao, H. et al. Ten-year outcomes following small incision lenticule extraction for up to -10Dioptres myopia. Clin Exp Optom , 1-6 (2023). Burton, M. J., Ramke, J., Marques, A. P. et al. The Lancet Global Health Commission on Global Eye Health: vision beyond 2020. Lancet Glob Health 9 , e489-e551 (2021). Cicinelli, M. V., Buchan, J. C., Nicholson, M., Varadaraj, V. & Khanna, R. C. Cataracts. Lancet 401 , 377-389 (2023). Chua, J., Koh, J. Y., Tan, A. G. et al. Ancestry, Socioeconomic Status, and Age-Related Cataract in Asians: The Singapore Epidemiology of Eye Diseases Study. Ophthalmology 122 , 2169-2178 (2015). Hambisa, M. T., Dolja-Gore, X. & Byles, J. E. A longitudinal analysis of factors associated with age-related cataract among older Australian women: a cohort study of 7851 older Australian women 79-90 years. Ir J Med Sci 192 , 1525-1536 (2023). Aramberri, J. Intraocular lens power calculation after corneal refractive surgery: double-K method. J Cataract Refract Surg 29 , 2063-2068 (2003). Wang, L. & Koch, D. D. Intraocular Lens Power Calculations in Eyes with Previous Corneal Refractive Surgery: Review and Expert Opinion. Ophthalmology 128 , e121-e131 (2021). Meziane Elotmani, A., Messerschmidt-Roth, A., Nehme, A., Muller, H. H. & Sekundo, W. Comparison of intraocular lens power calculation formulas with and without total keratometry and ray tracing in patients with previous myopic SMILE. J Cataract Refract Surg 49 , 467-473 (2023). Lazaridis, A., Schraml, F., Preussner, P. R. & Sekundo, W. Predictability of intraocular lens power calculation after small-incision lenticule extraction for myopia. J Cataract Refract Surg 47 , 304-310 (2021). Wang, Q., Jiang, W., Lin, T. et al. Accuracy of intraocular lens power calculation formulas in long eyes: a systematic review and meta-analysis. Clin Exp Ophthalmol 46 , 738-749 (2018). Yin, S., Guo, C., Qiu, K. et al. Assessment of the influence of keratometry on intraocular lens calculation formulas in long axial length eyes. Int Ophthalmol 42 , 3211-3219 (2022). Melles, R. B., Holladay, J. T. & Chang, W. J. Accuracy of Intraocular Lens Calculation Formulas. Ophthalmology 125 , 169-178 (2018). Hoffer, K. J. & Savini, G. IOL Power Calculation in Short and Long Eyes. Asia Pac J Ophthalmol (Phila) 6 , 330-331 (2017). Roberts, T. V., Hodge, C., Sutton, G., Lawless, M. & contributors to the Vision Eye Institute, I. O. L. o. r. Comparison of Hill-radial basis function, Barrett Universal and current third generation formulas for the calculation of intraocular lens power during cataract surgery. Clin Exp Ophthalmol 46 , 240-246 (2018). Omoto, M., Sugawara, K., Torii, H. et al. Investigating the Prediction Accuracy of Recently Updated Intraocular Lens Power Formulas with Artificial Intelligence for High Myopia. J Clin Med 11 (2022). Li, C., Wang, M., Feng, R. et al. Comparison of Formula-Specific Factors and Artificial Intelligence Formulas with Axial Length Adjustments in Bilateral Cataract Patients with Long Axial Length. Ophthalmol Ther 11 , 1869-1881 (2022). Sato, T., Iimori, E. & Hayashi, K. Prospective comparison of accuracy of intraocular lens calculation formulas in phacovitrectomy: a pilot study in a real-world clinical practice. Graefes Arch Clin Exp Ophthalmol 261 , 77-84 (2023). Cheng, A. M. S., Yin, H. Y., Davenport, C. & Walter, K. Clinical outcome of diffractive multifocal lens versus monofocal lens in post-laser in situ keratomileusis patients: A retrospective, comparative study. Indian J Ophthalmol 71 , 779-783 (2023). Gasparian, S. A., Nassiri, S., You, H., Vercio, A. & Hwang, F. S. Intraoperative aberrometry compared to preoperative Barrett True-K formula for intraocular lens power selection in eyes with prior refractive surgery. Sci Rep 12 , 7357 (2022). Lanza, M., Ruggiero, A., Ha, J., Simonelli, F. & Kane, J. X. Accuracy of Formulas for Intraocular Lens Power Calculation After Myopic Refractive Surgery. J Refract Surg 38 , 443-449 (2022). Mechleb, N., Debellemanière, G., Gauvin, M. et al. Using the First-Eye Back-Calculated Effective Lens Position to Improve Refractive Outcome of the Second Eye. J Clin Med 12 , 184 (2022). Hashemi, H., Roberts, C. J., Elsheikh, A. et al. Using the First-Eye Back-Calculated Effective Lens Position to Improve Refractive Outcome of the Second Eye: A Matched Comparison Study. Transl Vis Sci Technol 12 , 184 (2023). Ferguson, T. J., Downes, R. A. & Randleman, J. B. IOL power calculations after LASIK or PRK: Barrett True-K biometer-only calculation strategy yields equivalent outcomes as a multiple formula approach. J Cataract Refract Surg 48 , 784-789 (2022). Abulafia, A., Hill, W. E., Koch, D. D., Wang, L. & Barrett, G. D. Accuracy of the Barrett True-K formula for intraocular lens power prediction after laser in situ keratomileusis or photorefractive keratectomy for myopia. J Cataract Refract Surg 42 , 363-369 (2016). Savini, G., Hoffer, K. J. & Barrett, G. D. Results of the Barrett True-K formula for IOL power calculation based on Scheimpflug camera measurements in eyes with previous myopic excimer laser surgery. J Cataract Refract Surg 46 , 1016-1019 (2020). Luft, N., Siedlecki, J., Schworm, B. et al. Intraocular Lens Power Calculation after Small Incision Lenticule Extraction. Sci Rep 10 , 5982 (2020). Savini, G., Barboni, P., Carbonelli, M., Ducoli, P. & Hoffer, K. J. Intraocular lens power calculation after myopic excimer laser surgery: Selecting the best method using available clinical data. J Cataract Refract Surg 41 , 1880-1888 (2015). Vrijman, V., Abulafia, A., van der Linden, J. W. et al. ASCRS calculator formula accuracy in multifocal intraocular lens implantation in hyperopic corneal refractive laser surgery eyes. J Cataract Refract Surg 45 , 582-586 (2019). Chen, X., Yuan, F. & Wu, L. Metaanalysis of intraocular lens power calculation after laser refractive surgery in myopic eyes. J Cataract Refract Surg 42 , 163-170 (2016). Ferrara, S., Crincoli, E., Savastano, A. et al. Refractive Outcomes With New Generation Formulas for IOL Power Calculation in Radial Keratotomy Patients. Cornea (2023). Farahat, Z., Zrira, N., Souissi, N. et al. Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion. Diagnostics (Basel) 13 , 1694 (2023). Wei, W., Southern, J., Zhu, K. et al. Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography. Sci Rep 13 , 8296 (2023). Miao, J., Yu, J., Zou, W. et al. Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion. Front Med (Lausanne) 9 , 794045 (2022). Sanchez-Linan, N., Perez-Rueda, A., Parron-Carreno, T., Nievas-Soriano, B. J. & Castro-Luna, G. Evaluation of biometric formulas in the calculation of intraocular lens according to axial length and type of the lens. Sci Rep 13 , 4678 (2023). Moshirfar, M., Durnford, K. M., Jensen, J. L. et al. Accuracy of Six Intraocular Lens Power Calculations in Eyes with Axial Lengths Greater than 28.0 mm. J Clin Med 11 , 5947 (2022). Masket, S. & Masket, S. E. Simple regression formula for intraocular lens power adjustment in eyes requiring cataract surgery after excimer laser photoablation. J Cataract Refract Surg 32 , 430-434 (2006). Zarei-Ghanavati, S., Jafarzadeh, S. V., Es'haghi, A. et al. Comparison of 110- and 145-microm Small-Incision Lenticule Extraction Cap Thickness: A Randomized Contralateral Eye Study. Cornea (2023). Fang, L., Jin, T., Cao, Y. et al. Biomechanical Responses of Different Cap Thicknesses of Corneas After Small Incision Lenticule Extraction: Finite Element Analysis. Transl Vis Sci Technol 12 , 5 (2023). Lv, X., Zhang, F., Song, Y. et al. Corneal biomechanical characteristics following small incision lenticule extraction for myopia and astigmatism with 3 different cap thicknesses. BMC Ophthalmol 23 , 42 (2023). Vrijman, V., Abulafia, A., van der Linden, J. W. et al. Evaluation of Different IOL Calculation Formulas of the ASCRS Calculator in Eyes After Corneal Refractive Laser Surgery for Myopia With Multifocal IOL Implantation. J Refract Surg 35 , 54-59 (2019). Afshar, M., Adelaine, S., Resnik, F. et al. Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults. JMIR Med Inform 11 , e44977 (2023). Danjo, Y. Modification of the Barrett Universal II formula by the combination of the actual total corneal power and virtual axial length. Graefes Arch Clin Exp Ophthalmol 261 , 1913-1921 (2023). Danjo, Y., Ohji, R. & Maeno, S. Lower refractive prediction accuracy of total keratometry using intraocular lens formulas loaded onto a swept-source optical biometer. Graefes Arch Clin Exp Ophthalmol 261 , 137-146 (2023). Chung, H. S., Chung, J. L., Kim, Y. J. et al. Comparing prediction accuracy between total keratometry and conventional keratometry in cataract surgery with refractive multifocal intraocular lens implantation. Sci Rep 11 , 19234 (2021). Anjou, M., Brezin, A. & Monnet, D. Total Keratometry Measured With a Swept-Source Optical Biometer Versus Anterior Keratometry: From Planning to Postoperative Results. J Refract Surg 39 , 257-264 (2023). Yeo, T. K., Heng, W. J., Pek, D., Wong, J. & Fam, H. B. Accuracy of intraocular lens formulas using total keratometry in eyes with previous myopic laser refractive surgery. Eye (Lond) 35 , 1705-1711 (2021). Khan, A., Rangu, N., Murphy, D. A. et al. Standard vs total keratometry for intraocular lens power calculation in cataract surgery combined with DMEK. J Cataract Refract Surg 49 , 239-245 (2023). Heath, M. T., Mulpuri, L., Kimiagarov, E. et al. IOL Power Calculations in Keratoconus Eyes Comparing Keratometry, Total Keratometry, and Newer Formulae. Am J Ophthalmol (2023). Guo, H., Hosseini-Moghaddam, S. M. & Hodge, W. Corneal biomechanical properties after SMILE versus FLEX, LASIK, LASEK, or PRK: a systematic review and meta-analysis. BMC Ophthalmol 19 , 167 (2019). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Nov, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Nov, 2023 Reviews received at journal 31 Oct, 2023 Reviewers agreed at journal 16 Oct, 2023 Reviewers agreed at journal 05 Sep, 2023 Reviews received at journal 29 Aug, 2023 Reviewers agreed at journal 29 Aug, 2023 Reviewers invited by journal 28 Aug, 2023 Editor assigned by journal 28 Aug, 2023 Editor invited by journal 23 Jun, 2023 Submission checks completed at journal 23 Jun, 2023 First submitted to journal 18 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3080000","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":212555496,"identity":"c06cff8d-d983-49f9-8791-74c83eb9b86e","order_by":0,"name":"Liangpin Li","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liangpin","middleName":"","lastName":"Li","suffix":""},{"id":212555497,"identity":"94a1f361-8126-4e45-bc57-be4173bc4425","order_by":1,"name":"Liyun Yuan","email":"","orcid":"","institution":"Nankai University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liyun","middleName":"","lastName":"Yuan","suffix":""},{"id":212555498,"identity":"db365a8e-7fa0-42df-9565-125d283099ab","order_by":2,"name":"Kun Yang","email":"","orcid":"","institution":"Tianjin Eye Institute, Tianjin Eye Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Yang","suffix":""},{"id":212555500,"identity":"23dc4926-7141-433f-8cf2-27d36092e329","order_by":3,"name":"Yanan Wu","email":"","orcid":"","institution":"Tianjin Eye Institute, Tianjin Eye Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Wu","suffix":""},{"id":212555502,"identity":"07d28cae-4688-4f81-ba12-a4b3384d01a2","order_by":4,"name":"Simayilijiang Alafati","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Simayilijiang","middleName":"","lastName":"Alafati","suffix":""},{"id":212555503,"identity":"89d5a910-c93d-4e5f-8825-59277253abee","order_by":5,"name":"Xia Hua","email":"","orcid":"","institution":"Tianjin Aier Eye Hospital, Tianjin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Hua","suffix":""},{"id":212555506,"identity":"d93a1f11-1b3d-4ae4-bb1c-801b1389a2de","order_by":6,"name":"Yan Wang","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wang","suffix":""},{"id":212555507,"identity":"76f6f06d-2105-46bf-8386-0f985b741a87","order_by":7,"name":"Xiaoyong Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFAD9h4wxcNHvBaeMwwMB4AUG/FaJHLAWhgIajG4kfzsMW8bg7y55NuDjz/m2MmwMTA/fHQDr5Y0c8OZbQyGO2fnJRsc3JYMdBibsXEOXi0JZhIf2xgSDG7nmEkc3MYM1MLDJo1fS/o3iUSQlptnQFrqidGSA7XlBg9Iy2HCWiTPvCmTnHGOwXDDmRxjg7PbjvOwMRPwC9/x9G3SPGUM8gbHzxg+qNxWbc/P3vzwMT4tCgeABCPbfyQhZjzKQUC+AUT+IaBqFIyCUTAKRjYAAOO0RhPe5+k1AAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaoyong","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2023-06-19 01:59:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3080000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3080000/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-47990-0","type":"published","date":"2023-11-23T15:00:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39259599,"identity":"2c0a5ccc-b53b-44e0-9060-2629b1d1434d","added_by":"auto","created_at":"2023-06-28 20:27:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140844,"visible":true,"origin":"","legend":"\u003cp\u003ea. Violin plot of prediction errors (PE) of 9 formulas. The formulas with the smallest PE were Kane-K (-1.373±0.78 D), Kane-TK (-0.64±0.61 D), and EVO-K (-0.53±0.50 D), and the formulas with the largest PE were Shammas-TK (1.58±0.68 D), BTK-TK (1.46±0.71 D), and m-Masket-TK (0.96±0.54 D). b. Violin plot of absolute prediction errors (AE) of 9 formulas.The formulas with the lowest AE were EVO-TK (0.40±0.29, range 0 to 1.23, MedAE=0.36 D), BTK-K (0.41±0.26, range 0.01 to 1.19, MedAE=0.35), and Masket-K (0.44±0.33, range 0.02 to 1.39, MedAE=0.37).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3080000/v1/4c4e622210f8adac9dc5b7a7.png"},{"id":39259597,"identity":"7baff2d3-f992-4bae-be60-159d791ac108","added_by":"auto","created_at":"2023-06-28 20:27:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53294,"visible":true,"origin":"","legend":"\u003cp\u003eStacked histogram analysis comparing the percentage of eyes within given prediction error ranges as compared with preoperative BUII. The formulas were sorted by the proportion of eyes within ±0.50D in descending order. The formulas with the highest proportion of AE \u0026lt; 0.5D were BTK-K (71.15%), EVO-TK (69.23%), and Masket-K (67.31%).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3080000/v1/980f0c6ff5211ee40419df81.png"},{"id":39260572,"identity":"18e1d698-15fb-4a5a-beb1-ddad99c78ea1","added_by":"auto","created_at":"2023-06-28 20:35:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83389,"visible":true,"origin":"","legend":"\u003cp\u003eMore detailed frequency profiles of the EVO-TK, BTK-K, and Masket-K formulas.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3080000/v1/34bdf577577380476eeca24c.png"},{"id":47146305,"identity":"1047aca9-e2c4-467b-9364-07c10181bf41","added_by":"auto","created_at":"2023-11-27 15:07:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":543456,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3080000/v1/db6e990c-a281-415d-8603-814ca30f3de5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of the accuracy of 9 intraocular lens power calculation formulas after SMILE in Chinese myopic eyes","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSmall incision lenticule extraction (SMILE) is the latest generation of refractive surgery and is widely utilized for treating refractive errors. SMILE involves creating a corneal lenticule within the stromal layer, which is subsequently removed through a small incision. This innovative technique eliminates the need for corneal flap preparation while ensuring enhanced precision and superior biomechanical outcomes compared to alternative methods\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. On the other hand, cataracts, as one of the most common causes of blindness worldwide\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, are very common in the elderly population, and studies have shown that the prevalence of cataracts in the population over 80 years old is as high as 60\u0026ndash;90%\u003csup\u003e4,5\u003c/sup\u003e. In the future, we can envision a substantial number of cataract patients who have previously undergone SMILE surgery. However, this presents a dilemma for surgeons. While these individuals hold high expectations for optimal postoperative visual quality, the corneal morphology undergoes changes because of the previous refractive surgery. Consequently, the accuracy and predictability of calculating the diopter for intraocular lens (IOL) are significantly reduced. What is even more challenging is the inability to rectify any refractive complications that may arise after the operation through additional laser surgery as keratectomy has already been performed.\u003c/p\u003e \u003cp\u003eAs one of the most important guarantees for the accurate calculation of IOL power, the IOL formula plays an important role. To be more suitable for different eye conditions, the IOL formula is constantly innovated. The conventional formula predicts the effective lens position (ELP) by corneal curvature\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, but the corneal curvature of the eyes after refractive surgery changes greatly, resulting in a large error\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. As a new method of refractive surgery, we cannot directly apply the data of previous Laser-assisted In Situ Keratomileusis (LASIK), so there is an urgent need to find accurate formulas for cataract patients with a history of SMILE. However, due to the short history of SMILE surgery and because most of the patients are young, a large enough number of cataract patients who have undergone SMILE surgery is unavailable, making it difficult to find the best formula directly. To solve this problem, the theoretical surgical model proposed by Lazaridis, A., et al. is the most reliable method in this kind of research at present\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The theoretical surgery involves optical biometry measurements before and after SMILE surgery, using the same formula to calculate and compare the pre- and postoperative measurements to evaluate the stability of various formulas, making it an ideal research method in the current situation. In this study, we improved upon this theoretical surgical model by using the preoperative result of the Barrett Universal II (BUII) formula as a baseline. BUII is one of the fifth generation IOL calculation formulas known for its high accuracy and stability in the target population, particularly in patients with medium to long axial lengths (25.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 mm in our study) \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. If the calculation accuracy in post-SMILE patients can approach the performance of BUII in patients without a history of refractive surgery, it would undoubtedly be a considerable success. Therefore, we used BUII for preoperative IOL power calculation, and the postoperative formula with results closest to BUII was considered more accurate.\u003c/p\u003e \u003cp\u003eAt present, the emergence of artificial intelligence (AI) calculation formulas based on big data has led to the development of all kinds of formulas, including the Kane, Emmetropia Verifying Optical 2.0 (EVO) and Pearl-DGS formulas\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The most important feature of this type of formula is its self-learning ability, and it is expected to make a breakthrough in the field of accurate computing after refractive surgery. In addition, some formulas, such as the Barrett True K (BTK), Haigis-L and Masket formulas, have been proven to be accurate after LASIK. Therefore, the purpose of this study is to explore the suitability of these formulas for patients after SMILE through the theoretical surgical model.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003e This research project was approved by the Ethics Committee of Tianjin Eye Hospital and was conducted according to the principles of the Helsinki Declaration. The study included 52 eyes of 26 patients with myopia who received refractive treatment at Tianjin Eye Hospital from October 2022 to March 2023. Each patient was informed of the research content and signed the informed consent form before the surgery.\u003c/p\u003e \u003cp\u003eAll patients were evaluated by ophthalmology before the operation, including uncorrected distance visual acuity and corrected distance visual acuity, manifest refraction, and cycloplegic refraction, slit lamp examination, optical biometrics with IOL Master700 (Carl Zeiss Meditec AG, Jena, Germany) and corneal tomography with Pentacam HR (OCULUSO ptikgerate: GmbH). Postoperative evaluation was performed at least 3 months after surgery, including uncorrected distance visual acuity and corrected distance visual acuity measurements, manifest refraction and IOLMaster700 measurements.\u003c/p\u003e \u003cp\u003eThe inclusion criteria included age more than 18 years, stable refraction 2 years before operation, corrected distance visual acuity of 20/20 or better, suspended use of soft contact lenses for more than 2 weeks, and suspended use of hard contact lenses for more than 4 weeks. The exclusion criteria were slit lamp examination for any corneal or lens opacity or pathological changes, previous corneal surgery, ocular trauma or intraocular surgery, severe dry eye, glaucoma, corneal disease or ocular infection, keratoconus or suspected keratoconus, remaining stromal expected\u0026thinsp;\u0026lt;\u0026thinsp;280 \u0026micro;m, posterior scleral staphyloma and so on. All operations were performed by the same experienced surgeon (Wang Y) using the VisuMax femtosecond laser (Carl Zeiss Meditec AG, Jena, Germany). There were no intraoperative or postoperative complications.\u003c/p\u003e \u003cp\u003eSince the lens and the posterior segment of the eye are almost unchanged except for the change in the cornea caused by SMILE procedure, the change in IOL power before and after the operation should theoretically be equal to the change in the corneal refractive power caused by the operation. In this study, a theoretical surgical approach simulating lens removal was adopted. Preoperative and postoperative optical biometry measurements were performed using the IOL Master 700. Preoperatively, we used BUII formula to calculate IOL power (the target refraction was selected as the equivalent spherical diopter of the patient before SMILE). Postoperatively, three different AI-based formulas, namely, Kane (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.iolformula.com\" target=\"_blank\"\u003ewww.iolformula.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.iolformula.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), EVO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.iolformula.com\" target=\"_blank\"\u003ewww.evoiolcalculator.com/calculator.aspx\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.evoiolcalculator.com/calculator.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Pearl-DGS (iolsolver.com/complex) as well as six commonly used formulas (both available at ASCRS online, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ascrs.org/tools\u003c/span\u003e\u003cspan address=\"https://ascrs.org/tools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), namely, BTK, Barrett True K no history (BTKNH), Masket, modified-Masket (m-Masket), Shammas, and Haigis-L formulas, were used to calculate the IOL power corresponding to a target refractive power of 0 D. All calculation results were rounded to two decimal places. Each formula's calculation result minus the preoperative BUII calculation result yielded the prediction error (PE), its absolute value represented the absolute prediction error (AE), and the median of AE was abbreviated as MedAE. During calculations, the keratometry (K) and total keratometry (TK) values were input into each formula separately, denoted as Kane-K/Kane-TK, BTK-K/BTK-TK, and so on. To maintain consistency among the formulas in IOL power calculations, the same model of IOL (AR40e, Johnson \u0026amp; Johnson Vision Care, Inc., A constant\u0026thinsp;=\u0026thinsp;118.71; A1\u0026thinsp;=\u0026thinsp;1.242, A2\u0026thinsp;=\u0026thinsp;0.2, A3\u0026thinsp;=\u0026thinsp;0.1) was chosen.\u003c/p\u003e \u003cp\u003eDescriptive statistics (i.e., mean, SD, and range) were performed using Microsoft Excel 2016 (Microsoft Corp.). Statistical analysis was conducted using SPSS 26.0 software (IBM Corp.; Armonk, NY, USA). The Kolmogorov‒Smirnov test was used to assess the distribution normality of the results of each calculation method, PE, and AE. The results were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and a one-sample t test was used to compare the differences in PE between various calculation methods and zero. One-way analysis of variance (ANOVA) was used to compare the differences in PE and AE among the three calculation methods. In cases where a significant overall difference was observed, pairwise comparisons between the AE in any two groups were conducted using one-sample t test or Friedman test. The Chi-square test was employed to compare the differences in the percentage of eyes falling within \u0026plusmn;\u0026thinsp;0.5 D and \u0026plusmn;\u0026thinsp;1.00 D of AE among the formulas. A p value of less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULT","content":"\u003cp\u003eThis study included a total of 26 patients (52 eyes, 11 males and 15 females) who received treatment at Tianjin Eye Hospital in Tianjin, China, from September 2022 to February 2023. The mean follow-up period after SMILE surgery was 4\u0026thinsp;\u0026plusmn;\u0026thinsp;2 months (ranging from 3 to 8 months). The age of the patients ranged from 18 to 35 years, with a mean age of 26.36\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15 years. The preoperative average central corneal thickness (CCT) was 546.3\u0026thinsp;\u0026plusmn;\u0026thinsp;31.57 \u0026micro;m (ranging from 494 to 620 \u0026micro;m), while the postoperative average CCT was 475.3\u0026thinsp;\u0026plusmn;\u0026thinsp;40.02 \u0026micro;m (ranging from 413 to 586 \u0026micro;m). The preoperative average spherical equivalent (SE) was \u0026minus;\u0026thinsp;4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 diopters (ranging from \u0026minus;\u0026thinsp;1.75 to -8.25 D), and the postoperative average SE was \u0026minus;\u0026thinsp;0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 D (+\u0026thinsp;0.125 D to -1.50 D). The preoperative average axial length (AL) was 25.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 mm (ranging from 23.97 to 27.53 mm), and the postoperative average AL was 25.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 mm (ranging from 23.86 to 27.44 mm). The preoperative average ACD was 3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.312 mm, and the postoperative average ACD was 3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.183 mm. The average reduction in ACD after surgery was 0.114\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 mm. All patients underwent SMILE surgery for myopia correction, and their postoperative visual acuity was 20/20 or better. Additional patient characteristics and biometric data are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographic and ocular biometry data before and after SMILE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEyes/patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52/26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM/F, 11/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(18, 25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81, (23.97, 27.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81, (23.86, 27.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeratometry (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCT (\u0026micro;m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e548.11\u0026thinsp;\u0026plusmn;\u0026thinsp;32.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e475.25\u0026thinsp;\u0026plusmn;\u0026thinsp;40.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eM\u0026thinsp;=\u0026thinsp;Male; F\u0026thinsp;=\u0026thinsp;Female; AL\u0026thinsp;=\u0026thinsp;Axial length; ACD\u0026thinsp;=\u0026thinsp;Anterior chamber depth; Preop\u0026thinsp;=\u0026thinsp;preoperative; Postop\u0026thinsp;=\u0026thinsp;postoperative; SE\u0026thinsp;=\u0026thinsp;spherical equivalent; CCT\u0026thinsp;=\u0026thinsp;central corneal thickness\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Kolmogorov‒Smirnov test showed that the calculation results and PE of each formula were normally distributed. Using one-way ANOVA to analyse the variance of 18 different data points, it was found that there was a significant difference in PE among the groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the AEs of each group, Kane-K, Kane-TK, EVO-TK, m-Masket-TK, BTK-K, BTK-TK, BTKNH-TK, Shammas, Shammas-TK, Haigis-L-K, and Haigis-L-TK were normally distributed, while the AEs of the other formula groups did not follow a normal distribution. The one-sample t test comparing PE with zero showed that, except for EVO-TK (p\u0026thinsp;=\u0026thinsp;0.951), Masket-TK (p\u0026thinsp;=\u0026thinsp;0.885), BTK-TK (p\u0026thinsp;=\u0026thinsp;0.059), and BTKNH-TK (p\u0026thinsp;=\u0026thinsp;0.625), which represent good consistency with the results of BUII, all other formulas were significantly different from zero (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The formulas with the smallest PE were Kane-K (-1.373\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78 D), Kane-TK (-0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 D), and EVO-K (-0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 D), indicating that these formulas yielded results smaller than the preoperative BUII results, which could result in hyperopic shift. The formulas with the largest PE were Shammas-TK (1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68 D), BTK-TK (1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71 D), and m-Masket-TK (0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54 D), suggesting a tendency for myopic shift postoperatively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe formulas with an average AE of less than 0.5 D were EVO-TK (0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29, range 0 to 1.23, MedAE\u0026thinsp;=\u0026thinsp;0.36 D), BTK-K (0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26, range 0.01 to 1.19, MedAE\u0026thinsp;=\u0026thinsp;0.35), and Masket-K (0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33, range 0.02 to 1.39, MedAE\u0026thinsp;=\u0026thinsp;0.37) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The Friedman test and paired t test showed no significant differences among the three formulas (EVO-TK: BTK-K, p\u0026thinsp;=\u0026thinsp;0.057; EVO-TK: Masket-K, p\u0026thinsp;=\u0026thinsp;0.941; BTK-K: Masket-K, p\u0026thinsp;=\u0026thinsp;0.905), indicating that these three formulas yielded results closest to the preoperative BUII results. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a more detailed frequency distribution of the PE in these three formulas. Among them, Masket-K demonstrates the highest distribution between \u0026minus;\u0026thinsp;0.25 D and 0.25 D, with a total of 20 eyes. EVO-TK shows a uniform distribution on both sides of 0D. On the other hand, BTK-K is predominantly distributed between \u0026minus;\u0026thinsp;0.75 D and \u0026minus;\u0026thinsp;0.25 D, indicating slightly lower results compared to BUII, which may result in mild hyperopic shift. The formulas with an average AE exceeding 1 D were Shammas-TK (1.584\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68, range 0.33 to 3.56, MedAE\u0026thinsp;=\u0026thinsp;1.61), Haigis-L-TK (1.464\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71, range 0.2 to 3.58, MedAE\u0026thinsp;=\u0026thinsp;1.41), and Kane-K (1.373\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78, range 0.08 to 3.03, MedAE\u0026thinsp;=\u0026thinsp;1.34). The AE for these three formulas followed a normal distribution, and pairwise t tests showed significant differences among all three pairs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). More detailed information can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRefractive prediction errors from BUII preoperative of 52 eyes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVO-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBTK-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasket-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em-Masket-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBTK-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBTKNH-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVO-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMASKET-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearl-DGS-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearl-DGS-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKane-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaigis-L-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBTKNH-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShammas-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em-Masket-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKane-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaigis-L-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShammas-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, when using TK values for calculations, the Friedman test and paired t tests showed that the inclusion of TK was superior to the inclusion of K values in the three artificial intelligence formulas. There were significant differences between the Kane and EVO formulas, while no significant difference was found in the Pearl-DGS formula (Kane: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, EVO: p\u0026thinsp;=\u0026thinsp;0.004, Pearl-DGS: p\u0026thinsp;=\u0026thinsp;0.218). The remaining six formulas showed the opposite trend, except for the BTK formula, where the accuracy significantly decreased when TK values were used (Masket p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, m-Masket p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, BTK p\u0026thinsp;=\u0026thinsp;0.587, BTKNH p\u0026thinsp;=\u0026thinsp;0.03, Shammas p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Haigis-L p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe distribution of AE within different ranges (\u0026lt;\u0026thinsp;0.5D, 0.5D-1.0D, 1D-2D, and \u0026gt;\u0026thinsp;2D) is shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e is the stacked histogram of these formulas. Among them, the formulas with the highest proportion of AE\u0026thinsp;\u0026lt;\u0026thinsp;0.5D were BTK-K (71.15%), EVO-TK (69.23%), and Masket-K (67.31%). And a more detailed frequency distribution of the EVO-TK, BTK-K, and Masket-K formulas is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The Chi-square test showed that the proportion of AE within 0.5 D for the BTK-K formula was significantly higher than that for the Masket-K formula (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;22.845, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no significant differences between BTK-K and EVO-TK or between EVO-TK and Masket-K (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.278, p\u0026thinsp;=\u0026thinsp;0.258; x2\u0026thinsp;=\u0026thinsp;0.93, p\u0026thinsp;=\u0026thinsp;0.76, respectively). The formulas with the highest proportion of absolute mean PE within 1.0 D were BTK-K (98.08%), Masket-K (96.15%), and EVO-TK (94.23%). The Chi-square test showed no significant differences among the three formulas (BTK-K and Masket-K: χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;0.84; BTK-K and EVO-K: χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;=\u0026thinsp;0.803; Masket-K and EVO-TK: χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.27, p\u0026thinsp;=\u0026thinsp;0.721). The formulas with a PE greater than 2 D were Pearl-DGS-TK (3.84%), Haigis-L-K (3.84%), m-Masket-TK (3.84%), Haigis-L-TK (17.30%), and Shammas-TK (21.15%), indicating that using these formulas may result in larger errors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe percentage of eyes falling within different ranges of AE among the formulas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0-0.5 D(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.5-1.0 D(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.0-1.5 D(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.5-2.0 D(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026plusmn;2.0 D(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBTK-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e71.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEVO-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e69.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMasket-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e63.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBTKNH-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e57.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003em-Masket-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e53.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePearl-DGS-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e51.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMasket-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e50.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEVO-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e48.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e19.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBTK-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e46.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHaigis-L-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e42.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e21.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePearl-DGS-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e42.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e32.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKane-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e42.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e13.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eShammas-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e38.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e23.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBTK-NO-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e32.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e28.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003em-Masket-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKane-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e19.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e42.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHaigis-L-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e30.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eShammas-TK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e23.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e34.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAlthough SMILE has become one of the mainstream refractive surgeries, there is limited knowledge about accurate IOL power calculation for cataract patients who have undergone SMILE surgery. Currently, most formulas for postrefractive surgery cases are based on LASIK or Photo Refractive Keratectomy procedures\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Even commonly used measurement devices hardly have options for post-SMILE measurements. Since its commercial application in 2011, millions of patients with refractive errors have undergone SMILE to eliminate the need for glasses. As time goes by, the significant number of patients has made it crucial to address the issue of accurate IOL power calculation for this patient group when they develop cataracts. The LASIK procedures involves creating a corneal flap using the femtosecond laser and then using the excimer laser to reshape the cornea, with an incision up to 20 mm. On the other hand, SMILE is entirely performed using the femtosecond laser and involves a small incision of only 2 mm\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Additionally, when correcting the refractive error of equal degrees, the excised corneal stroma during SMILE is generally thicker than that in LASIK. Due to these differences, we cannot directly apply the research experience from LASIK to SMILE patients.\u003c/p\u003e \u003cp\u003eIn our study, we found that the BTK-K, Masket-K, and EVO-TK formulas closely approximated the preoperative results obtained with BUII, with all three formulas having an average AE of less than 0.5D. The BTK formula includes a postrefractive surgery mode, while the Masket formula is specifically designed for refractive surgery. BTK, developed based on BUII, has demonstrated good performance in various retrospective consecutive case series studies. Ferguson et al. reported that BTK outperformed other ASCRS formulas in both myopic and hyperopic post laser refractive surgery patients (96 post myopic eyes and 47 post hyperopic eyes) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Abulafia et al. found that BTK had significantly smaller prediction errors and a higher percentage of eyes within \u0026plusmn;\u0026thinsp;0.50 D than the Shammas and Haigis-L formulas in postmyopic LASIK cases (80 postmyopic eyes) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Notably, Savini et al. found that BTK yielded the best results when combined with measured posterior corneal power (PK, obtained with Pentacam) in cases with available historical data, while using PK without historical data led to larger errors (50 postmyopic eyes) \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Similarly, the Masket formula has been widely proven for its accuracy in post-LASIK patients, with several studies showing similar performance to the BTK formula\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Wu et al.'s meta-analysis demonstrated that the Masket formula was more accurate than the Haigis formula in postrefractive surgery patients\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Savini et al.'s prospective study found that the Masket formula may be the most reliable in postrefractive surgery cases lacking preoperative keratometry data but with known refractive change\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong the three formulas, EVO-TK, the only AI-based formula supported by a large dataset, also yielded satisfactory results. Consistent with this, previous studies have demonstrated excellent performance of the EVO formula in cases with various corneal abnormalities. Ferrara et al. found that the EVO formula showed higher accuracy in post-RK cataract patients than the SRK/T and BTK formulas in a study involving 27 patients\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In another retrospective study of 110 patients with congenital cataracts, Lin et al. found that the EVO formula performed best in children, especially those under 24 months of age and with an axial length less than 21 mm\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. As a new-generation formula utilizing big data and AI, the EVO formula exhibits excellent performance under various axial length conditions and can provide satisfactory results in complex and extreme ocular conditions\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The involvement of AI, especially deep learning, will help refine IOL formulas. The application of AI technology combined with big data in ophthalmology has become a trend. Miao et al. developed a model based on patient fundus photography data that surpassed the performance of resident doctors in determining ischaemia type and nonperfusion area and was comparable to senior doctors\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In a study by Farahat et al., two different AI systems were found to outperform resident doctors in diagnosing diabetic retinopathy. Wei et al. successfully applied AI to the screening of wet age-related macular degeneration by using deep learning on optical coherence tomography scan data from eight patients\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The time and resources of clinical doctors and researchers are limited, but once algorithms and AI models are established, AI can continuously learn and improve. Although the Kane formula based on AI and cloud computing has shown excellent performance in patients with long axial length and no history of refractive surgery\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, it exhibited significant differences in this study compared to the expected results. We believe there are three possible reasons for this: 1) the formula lacks a specific postrefractive surgery module, preventing the use of historical data despite including CCT as a parameter; 2) the formula innovatively uses sex as a mandatory parameter but does not include WTW (white to white); 3) the development of the Kane formula was based on data from over 30,000 Caucasian individuals, while our study included only Northern Chinese individuals of East Asian descent. We hope that in the future, the Kane formula can incorporate more cases of East Asian individuals and include postrefractive surgery modules.\u003c/p\u003e \u003cp\u003eThe aforementioned three formulas utilize historical data and include preoperative SE and postoperative SE as parameters to correct the calculation results, highlighting the value of accurate historical data in calculations for postrefractive surgery patients. The Masket formula, specifically designed for refractive surgery, uses the following equation to correct the final result: IOLpost + (RC x 0.326)\u0026thinsp;+\u0026thinsp;0.101 (where IOLpost is the calculated IOL power following refractive surgery and RC is the refractive change at the corneal plane) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The specific algorithms for the EVO and BTK formulas have not been disclosed. Current mainstream formulas typically accommodate only preoperative and postoperative equivalent spherical powers, with PERAL-DGS being the one of the few formulas that incorporates preoperative corneal curvature radius as a calculation parameter. Preoperative corneal curvature can serve as a reference for calculating ELP and is an important parameter in the double-K method\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. We believe that incorporating this parameter into the calculation formula will contribute to more accurate calculations, especially for patients with abnormal preoperative corneal curvature. There is still controversy regarding whether different corneal flap thicknesses affect the Corvis ST Biomechanical Index (CBI). Different corneal flap thicknesses may lead to differences in corneal CBI and stiffness parameters at first applanation\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Fang et al. found no influence of different flap thicknesses on CBI after SMILE, while another study showed significant differences in CBI and stiffness parameters at first applanation between 110 \u0026micro;m cap and 145 \u0026micro;m cap\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Lv et al. also found differences in CBI between 110 \u0026micro;m, 120 \u0026micro;m, and 130 \u0026micro;m caps\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Several studies have shown that the application of historical data improves calculation accuracy in patients after LASIK\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, but the limitation lies in the difficulty of obtaining such data due to long intervals between cataract surgery and refractive surgery. It is not feasible to rely on every patient to keep their own medical records. We believe that storing standardized patient preoperative and postoperative examination data in an online database in the form of electronic data would provide the greatest benefit to patients\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we found that the Kane and EVO formulas performed better when using TK, while the traditional Masket, m-Masket, BTKNH, Haigis, and Shammas formulas should use K instead of TK. As a nonmeasured value, ELP is derived based on corneal curvature as the primary reference value. Theoretically, for the same IOL, the ELP remains constant. If the ELP prediction tends towards the cornea, the actual power obtained will be higher, leading to a myopic shift when the IOL is positioned in its actual location. Conversely, a hyperopic shift may occur. The accuracy of corneal curvature directly affects the predicted ELP. The IOL Master 700 calculates K using the traditional telecentric keratometry method, while TK is derived from swept-source OCT measurements of the posterior corneal surface combined with K values, rather than being a direct measurement. Theoretically, TK should be more accurate and closer to the actual corneal power. However, some literature suggests that simply incorporating TK values may not be suitable for all formulas\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In a clinical retrospective comparative study, Danjo et al. found that in cases of single-focus IOL implantation (225 eyes of 225 patients, IOL Master 700), K provided better accuracy than TK for various axial lengths in routine cataract patients when using formulas such as BUII, Haigis, SRK/T, Holladay 2, and Hoffer Q\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Chung et al. also found that in the selection of multifocal IOLs (543 eyes of 543 patients, IOL Master 700), K should be used instead of TK with formulas such as BUII, Haigis, SRK/T, and Holladay 2\u003csup\u003e44\u003c/sup\u003e. It appears that TK does not provide an advantage in routine cataract patients. However, in the selection of toric IOLs, combining TK with the Barrett toric formula can reduce the error in predicted residual astigmatism (247 eyes of 180 patients, IOL Master 700) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Additionally, Yeo et al. found in a study of postrefractive surgery patients (64 eyes of 49 patients, IOL Master 700) that formulas such as BTK, Haigis-L, Shammas-PL, EVO, Hoffer Q, Holladay I, and SRK/T yielded better results when TK was used\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Similarly, studies have demonstrated that TK is superior to K in cases with complex corneal conditions such as combined corneal endothelial diseases and keratoconus\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Therefore, in designing this study, we used BUII with K preoperatively and compared TK and K postoperatively. We found that except for the EVO and Kane formulas, which performed better when using TK, the remaining formulas had larger errors. Further research is needed to determine whether TK should be used in postrefractive surgery patients. In the process of IOL power calculation, the corneal refractive power and axial length are fixed, and even in postrefractive surgery patients, the IOL Master 700 can accurately measure these two parameters.\u003c/p\u003e \u003cp\u003eIn our study, we selected measurement data from patients at least 3 months postoperatively because the corneal shape stabilizes after 3 months following SMILE surgery, with minimal changes over a certain period\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Xia et al. demonstrated the long-term stability and efficacy of SMILE surgery through a 10-year follow-up of patients who underwent the procedure\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Furthermore, since the K values for the most challenging ELP prediction are historical data that do not change, these formulas may still be applicable even if future changes in corneal shape occur. However, importantly, any formula can have deviations. This is where the advantage of comparing multiple formulas lies: obtaining similar results from different formulas provides us with confidence in selecting the IOL power, reducing the rate of significant refractive surprises. Although AI-based methods relying on big data require substantial data support from successful cases, they have not yet surpassed traditional formulas at this stage. However, with continuous deep learning, we believe that AI formulas will make significant progress. Additionally, we look forwards to AI systems that can assist us in selecting most reasonable results when different formulas yield disparate outcomes.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this study, we aimed to investigate the predictive accuracy of various formulas for IOL power calculation in post-SMILE patients. The results revealed that the BTK-K, Masket-K, and EVO-TK formulas showed the closest agreement with the BUII baseline results, with AE values below 0.5 D. Further research is needed to validate the efficacy of these formulas in a larger population. The utilization of AI and deep learning in ophthalmology is a growing trend, and it has the potential to refine IOL formulas. It is essential to establish standardized electronic databases for storing patients' preoperative data to facilitate accurate calculations and improve patient outcomes. Future studies should continue to explore the role of AI models and refine the selection of IOL power calculation formulas in post-SMILE patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFUNDING\u003c/h2\u003e\n\u003cp\u003eThis research was funded by the Natural Science Foundation of Tianjin City, China (No. 21JCZDJC01250). The authors sincerely treasured the comments and suggestions from reviewers.\u003c/p\u003e\n\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e\n\u003cp\u003eThe authors thank all participants of this study for sharing their time and experiences.\u003c/p\u003e\n\u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXia, F., Chen, Z., Miao, H.\u003cem\u003e et al.\u003c/em\u003e Ten-year outcomes following small incision lenticule extraction for up to -10Dioptres myopia. \u003cem\u003eClin Exp Optom\u003c/em\u003e, 1-6 (2023).\u003c/li\u003e\n\u003cli\u003eBurton, M. J., Ramke, J., Marques, A. P.\u003cem\u003e et al.\u003c/em\u003e The Lancet Global Health Commission on Global Eye Health: vision beyond 2020. \u003cem\u003eLancet Glob Health\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e489-e551 (2021).\u003c/li\u003e\n\u003cli\u003eCicinelli, M. V., Buchan, J. C., Nicholson, M., Varadaraj, V. \u0026amp; Khanna, R. C. Cataracts. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e401\u003c/strong\u003e, 377-389 (2023).\u003c/li\u003e\n\u003cli\u003eChua, J., Koh, J. Y., Tan, A. G.\u003cem\u003e et al.\u003c/em\u003e Ancestry, Socioeconomic Status, and Age-Related Cataract in Asians: The Singapore Epidemiology of Eye Diseases Study. \u003cem\u003eOphthalmology\u003c/em\u003e \u003cstrong\u003e122\u003c/strong\u003e, 2169-2178 (2015).\u003c/li\u003e\n\u003cli\u003eHambisa, M. T., Dolja-Gore, X. \u0026amp; Byles, J. E. A longitudinal analysis of factors associated with age-related cataract among older Australian women: a cohort study of 7851 older Australian women 79-90 years. \u003cem\u003eIr J Med Sci\u003c/em\u003e \u003cstrong\u003e192\u003c/strong\u003e, 1525-1536 (2023).\u003c/li\u003e\n\u003cli\u003eAramberri, J. Intraocular lens power calculation after corneal refractive surgery: double-K method. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 2063-2068 (2003).\u003c/li\u003e\n\u003cli\u003eWang, L. \u0026amp; Koch, D. D. Intraocular Lens Power Calculations in Eyes with Previous Corneal Refractive Surgery: Review and Expert Opinion. \u003cem\u003eOphthalmology\u003c/em\u003e \u003cstrong\u003e128\u003c/strong\u003e, e121-e131 (2021).\u003c/li\u003e\n\u003cli\u003eMeziane Elotmani, A., Messerschmidt-Roth, A., Nehme, A., Muller, H. H. \u0026amp; Sekundo, W. Comparison of intraocular lens power calculation formulas with and without total keratometry and ray tracing in patients with previous myopic SMILE. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 467-473 (2023).\u003c/li\u003e\n\u003cli\u003eLazaridis, A., Schraml, F., Preussner, P. R. \u0026amp; Sekundo, W. Predictability of intraocular lens power calculation after small-incision lenticule extraction for myopia. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 304-310 (2021).\u003c/li\u003e\n\u003cli\u003eWang, Q., Jiang, W., Lin, T.\u003cem\u003e et al.\u003c/em\u003e Accuracy of intraocular lens power calculation formulas in long eyes: a systematic review and meta-analysis. \u003cem\u003eClin Exp Ophthalmol\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 738-749 (2018).\u003c/li\u003e\n\u003cli\u003eYin, S., Guo, C., Qiu, K.\u003cem\u003e et al.\u003c/em\u003e Assessment of the influence of keratometry on intraocular lens calculation formulas in long axial length eyes. \u003cem\u003eInt Ophthalmol\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 3211-3219 (2022).\u003c/li\u003e\n\u003cli\u003eMelles, R. B., Holladay, J. T. \u0026amp; Chang, W. J. Accuracy of Intraocular Lens Calculation Formulas. \u003cem\u003eOphthalmology\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e, 169-178 (2018).\u003c/li\u003e\n\u003cli\u003eHoffer, K. J. \u0026amp; Savini, G. IOL Power Calculation in Short and Long Eyes. \u003cem\u003eAsia Pac J Ophthalmol (Phila)\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 330-331 (2017).\u003c/li\u003e\n\u003cli\u003eRoberts, T. V., Hodge, C., Sutton, G., Lawless, M. \u0026amp; contributors to the Vision Eye Institute, I. O. L. o. r. Comparison of Hill-radial basis function, Barrett Universal and current third generation formulas for the calculation of intraocular lens power during cataract surgery. \u003cem\u003eClin Exp Ophthalmol\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 240-246 (2018).\u003c/li\u003e\n\u003cli\u003eOmoto, M., Sugawara, K., Torii, H.\u003cem\u003e et al.\u003c/em\u003e Investigating the Prediction Accuracy of Recently Updated Intraocular Lens Power Formulas with Artificial Intelligence for High Myopia. \u003cem\u003eJ Clin Med\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eLi, C., Wang, M., Feng, R.\u003cem\u003e et al.\u003c/em\u003e Comparison of Formula-Specific Factors and Artificial Intelligence Formulas with Axial Length Adjustments in Bilateral Cataract Patients with Long Axial Length. \u003cem\u003eOphthalmol Ther\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1869-1881 (2022).\u003c/li\u003e\n\u003cli\u003eSato, T., Iimori, E. \u0026amp; Hayashi, K. Prospective comparison of accuracy of intraocular lens calculation formulas in phacovitrectomy: a pilot study in a real-world clinical practice. \u003cem\u003eGraefes Arch Clin Exp Ophthalmol\u003c/em\u003e \u003cstrong\u003e261\u003c/strong\u003e, 77-84 (2023).\u003c/li\u003e\n\u003cli\u003eCheng, A. M. S., Yin, H. Y., Davenport, C. \u0026amp; Walter, K. Clinical outcome of diffractive multifocal lens versus monofocal lens in post-laser in situ keratomileusis patients: A retrospective, comparative study. \u003cem\u003eIndian J Ophthalmol\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 779-783 (2023).\u003c/li\u003e\n\u003cli\u003eGasparian, S. A., Nassiri, S., You, H., Vercio, A. \u0026amp; Hwang, F. S. Intraoperative aberrometry compared to preoperative Barrett True-K formula for intraocular lens power selection in eyes with prior refractive surgery. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 7357 (2022).\u003c/li\u003e\n\u003cli\u003eLanza, M., Ruggiero, A., Ha, J., Simonelli, F. \u0026amp; Kane, J. X. Accuracy of Formulas for Intraocular Lens Power Calculation After Myopic Refractive Surgery. \u003cem\u003eJ Refract Surg\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 443-449 (2022).\u003c/li\u003e\n\u003cli\u003eMechleb, N., Debellemani\u0026egrave;re, G., Gauvin, M.\u003cem\u003e et al.\u003c/em\u003e Using the First-Eye Back-Calculated Effective Lens Position to Improve Refractive Outcome of the Second Eye. \u003cem\u003eJ Clin Med\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 184 (2022).\u003c/li\u003e\n\u003cli\u003eHashemi, H., Roberts, C. J., Elsheikh, A.\u003cem\u003e et al.\u003c/em\u003e Using the First-Eye Back-Calculated Effective Lens Position to Improve Refractive Outcome of the Second Eye: A Matched Comparison Study. \u003cem\u003eTransl Vis Sci Technol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 184 (2023).\u003c/li\u003e\n\u003cli\u003eFerguson, T. J., Downes, R. A. \u0026amp; Randleman, J. B. IOL power calculations after LASIK or PRK: Barrett True-K biometer-only calculation strategy yields equivalent outcomes as a multiple formula approach. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 784-789 (2022).\u003c/li\u003e\n\u003cli\u003eAbulafia, A., Hill, W. E., Koch, D. D., Wang, L. \u0026amp; Barrett, G. D. Accuracy of the Barrett True-K formula for intraocular lens power prediction after laser in situ keratomileusis or photorefractive keratectomy for myopia. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 363-369 (2016).\u003c/li\u003e\n\u003cli\u003eSavini, G., Hoffer, K. J. \u0026amp; Barrett, G. D. Results of the Barrett True-K formula for IOL power calculation based on Scheimpflug camera measurements in eyes with previous myopic excimer laser surgery. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 1016-1019 (2020).\u003c/li\u003e\n\u003cli\u003eLuft, N., Siedlecki, J., Schworm, B.\u003cem\u003e et al.\u003c/em\u003e Intraocular Lens Power Calculation after Small Incision Lenticule Extraction. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 5982 (2020).\u003c/li\u003e\n\u003cli\u003eSavini, G., Barboni, P., Carbonelli, M., Ducoli, P. \u0026amp; Hoffer, K. J. Intraocular lens power calculation after myopic excimer laser surgery: Selecting the best method using available clinical data. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 1880-1888 (2015).\u003c/li\u003e\n\u003cli\u003eVrijman, V., Abulafia, A., van der Linden, J. W.\u003cem\u003e et al.\u003c/em\u003e ASCRS calculator formula accuracy in multifocal intraocular lens implantation in hyperopic corneal refractive laser surgery eyes. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 582-586 (2019).\u003c/li\u003e\n\u003cli\u003eChen, X., Yuan, F. \u0026amp; Wu, L. Metaanalysis of intraocular lens power calculation after laser refractive surgery in myopic eyes. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 163-170 (2016).\u003c/li\u003e\n\u003cli\u003eFerrara, S., Crincoli, E., Savastano, A.\u003cem\u003e et al.\u003c/em\u003e Refractive Outcomes With New Generation Formulas for IOL Power Calculation in Radial Keratotomy Patients. \u003cem\u003eCornea\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eFarahat, Z., Zrira, N., Souissi, N.\u003cem\u003e et al.\u003c/em\u003e Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion. \u003cem\u003eDiagnostics (Basel)\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1694 (2023).\u003c/li\u003e\n\u003cli\u003eWei, W., Southern, J., Zhu, K.\u003cem\u003e et al.\u003c/em\u003e Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 8296 (2023).\u003c/li\u003e\n\u003cli\u003eMiao, J., Yu, J., Zou, W.\u003cem\u003e et al.\u003c/em\u003e Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 794045 (2022).\u003c/li\u003e\n\u003cli\u003eSanchez-Linan, N., Perez-Rueda, A., Parron-Carreno, T., Nievas-Soriano, B. J. \u0026amp; Castro-Luna, G. Evaluation of biometric formulas in the calculation of intraocular lens according to axial length and type of the lens. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 4678 (2023).\u003c/li\u003e\n\u003cli\u003eMoshirfar, M., Durnford, K. M., Jensen, J. L.\u003cem\u003e et al.\u003c/em\u003e Accuracy of Six Intraocular Lens Power Calculations in Eyes with Axial Lengths Greater than 28.0 mm. \u003cem\u003eJ Clin Med\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 5947 (2022).\u003c/li\u003e\n\u003cli\u003eMasket, S. \u0026amp; Masket, S. E. Simple regression formula for intraocular lens power adjustment in eyes requiring cataract surgery after excimer laser photoablation. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 430-434 (2006).\u003c/li\u003e\n\u003cli\u003eZarei-Ghanavati, S., Jafarzadeh, S. V., Es\u0026apos;haghi, A.\u003cem\u003e et al.\u003c/em\u003e Comparison of 110- and 145-microm Small-Incision Lenticule Extraction Cap Thickness: A Randomized Contralateral Eye Study. \u003cem\u003eCornea\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eFang, L., Jin, T., Cao, Y.\u003cem\u003e et al.\u003c/em\u003e Biomechanical Responses of Different Cap Thicknesses of Corneas After Small Incision Lenticule Extraction: Finite Element Analysis. \u003cem\u003eTransl Vis Sci Technol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 5 (2023).\u003c/li\u003e\n\u003cli\u003eLv, X., Zhang, F., Song, Y.\u003cem\u003e et al.\u003c/em\u003e Corneal biomechanical characteristics following small incision lenticule extraction for myopia and astigmatism with 3 different cap thicknesses. \u003cem\u003eBMC Ophthalmol\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 42 (2023).\u003c/li\u003e\n\u003cli\u003eVrijman, V., Abulafia, A., van der Linden, J. W.\u003cem\u003e et al.\u003c/em\u003e Evaluation of Different IOL Calculation Formulas of the ASCRS Calculator in Eyes After Corneal Refractive Laser Surgery for Myopia With Multifocal IOL Implantation. \u003cem\u003eJ Refract Surg\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 54-59 (2019).\u003c/li\u003e\n\u003cli\u003eAfshar, M., Adelaine, S., Resnik, F.\u003cem\u003e et al.\u003c/em\u003e Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults. \u003cem\u003eJMIR Med Inform\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, e44977 (2023).\u003c/li\u003e\n\u003cli\u003eDanjo, Y. Modification of the Barrett Universal II formula by the combination of the actual total corneal power and virtual axial length. \u003cem\u003eGraefes Arch Clin Exp Ophthalmol\u003c/em\u003e \u003cstrong\u003e261\u003c/strong\u003e, 1913-1921 (2023).\u003c/li\u003e\n\u003cli\u003eDanjo, Y., Ohji, R. \u0026amp; Maeno, S. Lower refractive prediction accuracy of total keratometry using intraocular lens formulas loaded onto a swept-source optical biometer. \u003cem\u003eGraefes Arch Clin Exp Ophthalmol\u003c/em\u003e \u003cstrong\u003e261\u003c/strong\u003e, 137-146 (2023).\u003c/li\u003e\n\u003cli\u003eChung, H. S., Chung, J. L., Kim, Y. J.\u003cem\u003e et al.\u003c/em\u003e Comparing prediction accuracy between total keratometry and conventional keratometry in cataract surgery with refractive multifocal intraocular lens implantation. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 19234 (2021).\u003c/li\u003e\n\u003cli\u003eAnjou, M., Brezin, A. \u0026amp; Monnet, D. Total Keratometry Measured With a Swept-Source Optical Biometer Versus Anterior Keratometry: From Planning to Postoperative Results. \u003cem\u003eJ Refract Surg\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 257-264 (2023).\u003c/li\u003e\n\u003cli\u003eYeo, T. K., Heng, W. J., Pek, D., Wong, J. \u0026amp; Fam, H. B. Accuracy of intraocular lens formulas using total keratometry in eyes with previous myopic laser refractive surgery. \u003cem\u003eEye (Lond)\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 1705-1711 (2021).\u003c/li\u003e\n\u003cli\u003eKhan, A., Rangu, N., Murphy, D. A.\u003cem\u003e et al.\u003c/em\u003e Standard vs total keratometry for intraocular lens power calculation in cataract surgery combined with DMEK. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 239-245 (2023).\u003c/li\u003e\n\u003cli\u003eHeath, M. T., Mulpuri, L., Kimiagarov, E.\u003cem\u003e et al.\u003c/em\u003e IOL Power Calculations in Keratoconus Eyes Comparing Keratometry, Total Keratometry, and Newer Formulae. \u003cem\u003eAm J Ophthalmol\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eGuo, H., Hosseini-Moghaddam, S. M. \u0026amp; Hodge, W. Corneal biomechanical properties after SMILE versus FLEX, LASIK, LASEK, or PRK: a systematic review and meta-analysis. \u003cem\u003eBMC Ophthalmol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 167 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"IOL power calculation formula, SMILE, refractive error, keratometry, total keratometry","lastPublishedDoi":"10.21203/rs.3.rs-3080000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3080000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs of 2021, over 2.8\u0026nbsp;million cases of small incision lenticule extraction (SMILE) procedures had been performed in China. However, there remains limited knowledge regarding the selection of intraocular lens (IOL) power calculation formulas for post-SMILE cataract patients. This study included 52 eyes of 26 myopic patients from northern China who underwent SMILE at Tianjin Eye Hospital from September 2022 to February 2023 and was designed to investigate the performance of multiple IOL calculation formulas in post-SMILE patients using a theoretical surgical model. We compared the postoperative results obtained from three artificial intelligence (AI)-based formulas and six conventional formulas provided by the American Society of Cataract and Refractive Surgery (ASCRS). These formulas were applied to calculate IOL power using both total keratometry (TK) and keratometry (K) values, and the results were compared to the preoperative results obtained from the Barrett Universal II (BUII) formula in SMILE cases. Among the evaluated formulas, the results obtained from Emmetropia Verifying Optical 2.0 Formula with TK (EVO-TK) (0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 D, range 0 to 1.23 D), Barrett True K with K (BTK-K, 0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 D, range 0.01 to 1.19 D), and Masket with K (Masket-K, 0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 D, range 0.02 to 1.39 D) demonstrated the closest proximity to BUII. Notably, the highest proportion of prediction errors within 0.5 D was observed with BTK-K (71.15%), EVO-TK (69.23%), and Masket-K (67.31%), with BTK-K showing a significantly higher proportion compared to Masket-K (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Our research indicates that in post-SMILE patients, EVO-TK, BTK-K, and Masket-K may yield more accurate calculation results. At the current stage, AI-based formulas do not demonstrate significant advantages over conventional formulas. However, the application of historical data can enhance the performance of these formulas.\u003c/p\u003e","manuscriptTitle":"Comparison of the accuracy of 9 intraocular lens power calculation formulas after SMILE in Chinese myopic eyes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-28 20:26:59","doi":"10.21203/rs.3.rs-3080000/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2023-11-02T18:20:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-10-31T20:45:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3021451e-9e58-4c03-a23f-dec0a28813ab","date":"2023-10-16T09:29:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"05f0fdc4-e04d-474a-8894-b3f96118a390","date":"2023-09-05T10:56:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-29T11:54:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36c442a9-8140-4d2c-8f74-9f56699cb51e","date":"2023-08-29T07:46:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-08-28T23:16:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-28T23:15:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-06-23T11:14:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-23T11:02:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-06-19T01:49:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e995db3e-987c-46d8-87a0-05bdb4c4cc98","owner":[],"postedDate":"June 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":22664434,"name":"Health sciences/Medical research"},{"id":22664435,"name":"Health sciences/Medical research/Study design"}],"tags":[],"updatedAt":"2023-11-27T15:04:02+00:00","versionOfRecord":{"articleIdentity":"rs-3080000","link":"https://doi.org/10.1038/s41598-023-47990-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-11-23 15:00:37","publishedOnDateReadable":"November 23rd, 2023"},"versionCreatedAt":"2023-06-28 20:26:59","video":"","vorDoi":"10.1038/s41598-023-47990-0","vorDoiUrl":"https://doi.org/10.1038/s41598-023-47990-0","workflowStages":[]},"version":"v1","identity":"rs-3080000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3080000","identity":"rs-3080000","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}