Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review

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One of the most common therapy, extracorporeal shock wave lithotripsy (ESWL), has a high failure rate. The failure rate can be significantly reduced by identifying the candidates most likely to benefit from ESWL, for example, by using machine learning (ML) algorithms. Decision tree analysis (DTA), artificial neural networks (ANN), and random forests (RF) represent a few of the machine learning approaches employed to forecast the stone-free outcome following ESWL. Methods 219 studies were searched through six electronic databases (CENTRAL, MEDLINE, EMBASE, EBSCO, Proquest, SCOPUS). We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and adhered to the Standards for Reporting Diagnostic Accuracy Studies (STARD). To evaluate the potential bias in all the studies, we utilized the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results 41,484 patients from 11 studies were included. The ML models highlight varying levels of diagnostic precision, with sensitivity spanning from 35-96%, and specificity ranging from 63-98.4%, and area under the curve falling between 0.49-0.96. It is shown in this study that the accuracy of RF and DTA in predicting stone-free status is superior than ANN. Conclusion ML is a comparable predictive method to statistical analysis in predicting stone-free status. Random forest method and DTA are superior MLs compared to ANN. Stone size, density, and 3D texture analysis are the most important variables to be considered in the ML models and should be included in the models to ensure accuracy of stone-free status prediction. 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F1000Research 2025, 14 :16 ( https://doi.org/10.12688/f1000research.152346.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Systematic Review Revised Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] Ficky . https://orcid.org/0000-0003-3813-8040 1 , Nur Rasyid https://orcid.org/0000-0003-4473-755X 1 , Widi Atmoko https://orcid.org/0000-0002-7793-7083 1 , Ponco Birowo https://orcid.org/0000-0003-2934-6753 1 Ficky . https://orcid.org/0000-0003-3813-8040 1 , Nur Rasyid https://orcid.org/0000-0003-4473-755X 1 , Widi Atmoko https://orcid.org/0000-0002-7793-7083 1 , Ponco Birowo https://orcid.org/0000-0003-2934-6753 1 PUBLISHED 26 Mar 2025 Author details Author details 1 Department of Urology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia Ficky . Roles: Conceptualization, Data Curation, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing Nur Rasyid Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Resources, Supervision Widi Atmoko Roles: Conceptualization, Data Curation, Investigation, Methodology, Software, Supervision, Writing – Review & Editing Ponco Birowo Roles: Conceptualization, Formal Analysis, Funding Acquisition, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the AI in Medicine and Healthcare collection. Abstract Background Urolithiasis is one of the most common urological diseases worldwide. One of the most common therapy, extracorporeal shock wave lithotripsy (ESWL), has a high failure rate. The failure rate can be significantly reduced by identifying the candidates most likely to benefit from ESWL, for example, by using machine learning (ML) algorithms. Decision tree analysis (DTA), artificial neural networks (ANN), and random forests (RF) represent a few of the machine learning approaches employed to forecast the stone-free outcome following ESWL. Methods 219 studies were searched through six electronic databases (CENTRAL, MEDLINE, EMBASE, EBSCO, Proquest, SCOPUS). We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and adhered to the Standards for Reporting Diagnostic Accuracy Studies (STARD). To evaluate the potential bias in all the studies, we utilized the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results 41,484 patients from 11 studies were included. The ML models highlight varying levels of diagnostic precision, with sensitivity spanning from 35-96%, and specificity ranging from 63-98.4%, and area under the curve falling between 0.49-0.96. It is shown in this study that the accuracy of RF and DTA in predicting stone-free status is superior than ANN. Conclusion ML is a comparable predictive method to statistical analysis in predicting stone-free status. Random forest method and DTA are superior MLs compared to ANN. Stone size, density, and 3D texture analysis are the most important variables to be considered in the ML models and should be included in the models to ensure accuracy of stone-free status prediction. READ ALL READ LESS Keywords artificial intelligence, stone-free status, urolithiasis, extracorporeal shock wave lithotripsy Corresponding Author(s) Nur Rasyid ( [email protected] ) Close Corresponding author: Nur Rasyid Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 . F et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: . F, Rasyid N, Atmoko W and Birowo P. Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.12688/f1000research.152346.2 ) First published: 03 Jan 2025, 14 :16 ( https://doi.org/10.12688/f1000research.152346.1 ) Latest published: 26 Mar 2025, 14 :16 ( https://doi.org/10.12688/f1000research.152346.2 ) Revised Amendments from Version 1 Reviewer 1 1. Updating the Search: We’ve decided to update the search to include studies published up to December 31, 2024, to ensure the review stays current with the latest advancements in machine learning for predicting stone-free status. 2. AI-Specific Evaluation Tool (APPRAISE-AI): We’ve recognized the value of using the APPRAISE-AI tool to assess the quality of machine learning studies. We have used APPRAISE-AI tool and made some changes. 3. Comparison with Non-ML Approaches: In response to the feedback, we will include a focus on comparing machine learning models with traditional methods, like clinical judgment, to evaluate their effectiveness in clinical practice. 4. Clarification on Stone-Free Status: We’ve addressed the variability in defining stone-free status across studies and committed to providing more clarity on how this status was determined, including the imaging modalities used. 5. Expanding the Limitations: We expanded the limitations section further and apply the APPRAISE-AI tool to uncover additional issues, with recommendations for improving future machine learning studies. Reviewer 2 1. Acknowledging Limitations: We have acknowledged the limitations of small sample sizes, retrospective data, and lack of external validation. We’ve emphasized that future research should focus on larger, prospective, multi-center studies to better validate machine learning models. 2. Exploring Hybrid Models: In response to the suggestion, we’ve included the idea of exploring hybrid models combining Random Forest and Artificial Neural Networks (ANN). This could enhance predictive accuracy, and we plan to consider this approach in future studies. 3. Methodological Transparency and Clinical Applicability: We’ve agreed on the importance of transparency in methodology and comparing machine learning models with clinical experts’ evaluations. We highlighted the need for standardized outcome reporting and reducing variability in ESWL outcomes to improve clinical applicability. Reviewer 1 1. Updating the Search: We’ve decided to update the search to include studies published up to December 31, 2024, to ensure the review stays current with the latest advancements in machine learning for predicting stone-free status. 2. AI-Specific Evaluation Tool (APPRAISE-AI): We’ve recognized the value of using the APPRAISE-AI tool to assess the quality of machine learning studies. We have used APPRAISE-AI tool and made some changes. 3. Comparison with Non-ML Approaches: In response to the feedback, we will include a focus on comparing machine learning models with traditional methods, like clinical judgment, to evaluate their effectiveness in clinical practice. 4. Clarification on Stone-Free Status: We’ve addressed the variability in defining stone-free status across studies and committed to providing more clarity on how this status was determined, including the imaging modalities used. 5. Expanding the Limitations: We expanded the limitations section further and apply the APPRAISE-AI tool to uncover additional issues, with recommendations for improving future machine learning studies. Reviewer 2 1. Acknowledging Limitations: We have acknowledged the limitations of small sample sizes, retrospective data, and lack of external validation. We’ve emphasized that future research should focus on larger, prospective, multi-center studies to better validate machine learning models. 2. Exploring Hybrid Models: In response to the suggestion, we’ve included the idea of exploring hybrid models combining Random Forest and Artificial Neural Networks (ANN). This could enhance predictive accuracy, and we plan to consider this approach in future studies. 3. Methodological Transparency and Clinical Applicability: We’ve agreed on the importance of transparency in methodology and comparing machine learning models with clinical experts’ evaluations. We highlighted the need for standardized outcome reporting and reducing variability in ESWL outcomes to improve clinical applicability. See the authors' detailed response to the review by Ranjit Barua See the authors' detailed response to the review by Jethro CC Kwong READ REVIEWER RESPONSES Introduction Urolithiasis ranks as the third most prevalent ailment encountered in urological practice, trailing only urinary tract infections and prostate irregularities in terms of occurrence. This condition stands as one of the most widespread urological maladies worldwide, with estimated prevalence rates spanning from 1% to 13% across various regions. 1 The global incidence, prevalence, and composition of urinary stones exhibit notable disparities and have undergone transformations over recent decades. Specifically, prevalence rates range from 7% to 13% in North America, 5% to 9% in Europe, and 1% to 5% in Asia. 2 This condition’s incidence is reported to range between 5% and 10%, occurring three times more frequently in men than in women. Individuals aged 30 to 50 face a heightened risk of developing urolithiasis, and it’s noteworthy that some patients experience recurrent stone formation. Most stones are naturally expelled through urination, with the duration of this process contingent on the stone’s size and location. Spontaneous passage occurs in approximately 80% of ureteral stones smaller than 5 mm. 3 Various methods are employed to actively remove kidney stones, such as extracorporeal shock wave lithotripsy (ESWL), ureterorenoscopy (URS), retrograde intrarenal surgery (RIRS), percutaneous nephrolithotomy (PNL), and open surgery. While surgical intervention plays a pivotal role in managing urinary stones, it does have certain drawbacks, including a temporary reduction in the patient’s quality of life, prolonged hospitalization, and a high cost burden, thus favoring ESWL as a less invasive alternative. 4 The concept of ESWL, involving the application of shock waves to disintegrate stones, was initially introduced in Russia during the 1950s and first implemented in humans in 1980. ESWL is considered the primary choice for addressing urinary stones, particularly those smaller than 2 cm, obviating the need for surgical procedures. Success rates for stones smaller than 2 cm hover around 70-80%. However, despite its prevalence, ESWL does exhibit a reported failure rate ranging from 30% to 89% after the initial session. 5 Effective identification of the most suitable candidates for ESWL can substantially curtail this failure rate, thereby optimizing treatment outcomes and judiciously utilizing limited medical resources. 3 To discern the predictive factors affecting ESWL outcomes, numerous studies have concentrated on statistical analyses of patient characteristics employing both bivariate and multivariate approaches. Several factors have emerged as influential in determining ESWL success, including anatomical features of the urinary tract, patient age, stone location, and size. 6 However, it’s important to acknowledge that these estimates are derived from statistical models based on cohort data, which possess certain drawbacks. These models may overlook significant factors that fall below an arbitrarily chosen numerical threshold, typically 5%. Moreover, as these models employ dichotomized values, clinicians often encounter challenges when attempting to integrate this prognostic information into routine clinical practice. In recent times, machine learning (ML) algorithms, a subset of artificial intelligence, have found increasing utility in the field of medicine. They are being widely applied to enhance the precision of disease diagnosis and prognosis. Essentially, machine learning comprises a set of techniques that enable computers to acquire knowledge from existing data sets, allowing them to make predictions. What makes machine learning particularly powerful is its capacity to rapidly analyze complex combinations of multiple variables. Among these machine learning algorithms, decision tree analysis (DTA) holds several advantages for medical applications. It is known for its simplicity in understanding and interpretation, as well as its suitability for both qualitative and quantitative data, whether textual or numeric. 7 Another noteworthy approach is the artificial neural network (ANN), a computational method inspired by biological brains, which approximates how biological neurons tackle problems through interconnected clusters and axons. Each neuron performs a summation function on input values, and the system is self-learning and adaptive, not relying on explicit programming. ANN excels in areas where conventional computer programs struggle to articulate solutions or recognize patterns. Once trained, the network can generate appropriate outputs for given inputs, even when confronted with patterns it has never encountered before. ANN is widely employed as an artificial intelligence technology across various domains. 8 Random Forests (RF) consist of an ensemble of multiple decision trees working together to establish output classifications. Numerous decision trees are trained using input data, and rather than relying exclusively on the single best-performing tree, a group of them is utilized collectively. Some of the MLs are illustrated in Figure 1 . This approach is akin to a committee making decisions collectively, which often results in greater prediction accuracy compared to a single decision tree. 9 , 10 Figure 1. Illustration of some of the machine learnings. 11 The aim of our systematic review is to evaluate the available ML methods capable of predicting the stone-free status in patients with urolithiasis following ESWL. Methods Condition and intervention Our objective is to assess the precision of artificial intelligence or machine learning in forecasting stone-free status in individuals with urolithiasis who have undergone ESWL treatment. Hence, this comprehensive review encompasses investigations that delve into machine learning techniques, including neural networks, to assist in computerized urography for the prediction of stone-free outcomes. Database searching and literature screening A study search was conducted in six electronic databases (CENTRAL, MEDLINE, EMBASE, EBSCO, Proquest, SCOPUS) on December 12 th 2022. PICO was used to make study tracking and finding the relevant literature easier. Specific keywords were used and adapting them as needed for each database ( Table 1 ). References from relevant systematic reviews were also explored. To be eligible for inclusion in this systematic review, studies were limited to those available in either Indonesian or English. Table 1. Search strategy. Database Keywords Result Date and time of attempt Cochrane Central Register of Controlled Trials (CENTRAL) Shockwave lithotripsy AND artificial intelligence 2 December 31 st 2022 MEDLINE ((urinary tract calculi OR urinary lithiasis OR urolithiasis OR kidney stone OR nephrolithiasis) AND (machine learning OR deep learning OR artificial intelligence OR AI)) AND (extracorporeal shockwave lithotripsy OR ESWL OR SWL OR shockwave lithotripsy) 96 December 31 st 2022 EMBASE (‘shock wave lithotripsy’ OR ‘extracorporeal shock wave lithotripsy’ OR ‘eswl’:ti,ab,kw) AND (‘artificial intelligence’ OR ‘machine learning’:ti,ab,kw) 28 December 31 st 2022 EBSCO (kidney stones or nephrolithiasis or renal calculi) AND (extracorporeal shock wave lithotripsy OR ESWL) AND (artificial intelligence or ai or a.i.) 26 December 31 st 2022 ProQuest (“Nefrolithiasis” OR “Kidney stone” OR “urolithiasis”) AND (“Extracorporeal Shock Wave Lithotripsy” OR “ESWL”) AND (“Machine Learning” OR “Artificial intelligence”) 63 December 31 st 2022 SCOPUS TITLE-ABS-KEY ((kidney AND stone OR urolithiasis OR nephrolithiasis) AND (extracorporeal AND shockwave AND lithotripsy OR eswl) AND (artificial AND intelligence OR are OR machine AND learning OR deep AND learning)) 4 December 31 st 2022 Study selection For this systematic review, we followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statements and the Standards for Reporting Diagnostic Accuracy Studies (STARD). We included randomized controlled trials (RCTs) or cohort studies that investigated the role of machine learning (ML) in predicting the stone-free status following extracorporeal shock wave lithotripsy (ESWL) for urinary system stones. These studies were required to report sensitivity, specificity, accuracy, or ROC (Receiver Operating Characteristic) data. Studies conducted on non-human subjects or those not available in English/Indonesian or without complete full-texts were excluded. Each author (NR, FF, PB, WD) independently assessed the eligibility of studies by reviewing their titles and abstracts and conducting a thorough analysis of the full texts. Any discrepancies among the authors were resolved through discussion. Extraction of data and outcome of interest Each author independently collected data using a predefined data extraction form. We gathered information on study characteristics, including patient demographics, sample sizes, and the specific machine learning techniques utilized, along with their diagnostic parameters. In cases of disagreements, consensus was reached through discussion. The primary objective of this systematic review is to evaluate the accuracy of AI/machine learning in diagnosing stone-free status. Our main outcome measures of interest are the sensitivity, specificity, and overall accuracy of AI/machine learning when compared to the diagnostic capabilities of urography alone. We employed the Review Manager 5.4 application to gather these outcomes and score the risk of bias in the included literatures. Methodological assessment This systematic review encompasses diagnostic investigations employing both experimental and observational study designs. To gauge the potential bias in all the studies, we employed the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results Literature search The initial search across six electronic databases yielded a total of 219 articles, of which 200 were duplicates. After screening the titles and abstracts of the remaining articles, we identified 19 studies that aligned with the criteria set by our systematic review’s PICO framework. However, upon conducting a thorough analysis of the full-text articles, only eleven met our PICO criteria, while the remaining eight studies did not meet the inclusion criteria for this review ( Figure 2 ). Figure 2. PRISMA flow chart of including articles. Study characteristics Eleven studies were included in this systematic review. Based on the population of each study, this systematic review involved 41,484 patients. These studies are spread across four continents, Europe, Asia, America, and Africa, most of which are conducted in Asia. The characteristics of the studies are summarized in Table 2 . Table 2. Summary of evidence. Author (Year) Factors included in model Stone Location Stone Size (mm) Machine Learning Model Total sample (Training/Test) (n) Outcomes Sensitivity Specificity Accuracy ROC AUC Michaels EK 12 et al. (1998) • Previous stone events • Metabolic abnormality (Hypercalciuria, hyperuricosuria) • Infection • Stone size • Directed medical therapy • Caliectasis/dilated collecting system • Residual fragments after ESWL Kidney, Ureter N/A ANN 98 (65/33) Stone-free status three months after treatment through abdominal radiographs 91% 92% 91% 0.964 Poulakis V 13 et al. (2003) • Age • BMI • No SWL sessions • SWL intensity • No of stones • Stone size • Stone surface area • Infundibular length • Infundibular diameter • Caliceal pelvic height • Lower infundibulopelvic angle • Infundibulouretero-pelvic angle • Urinary transport type Kidney (Lower pole) 14.79 ± 6.53 ANN 701 (600/101) Stone-free status after six months 91% 90% 92% N/A Hamid A 14 et al. (2003) • Clinical (Age, Body Habitus) • 24-hour urinary volume • Size of stones • Hydronephrosis • ESWL (total number of sittings, total number of shocks, mean power, mean frequency) Kidney N/A ANN 82 (60/22) Stone-free status during 1.5 years of the study period through Intravenous Urogram (IVU) N/A N/A 75% N/A Gomha MA 15 et al. (2004) • Age • Sex • Kidney Side • Stone Location • Stent • Renal Anatomy • Stone nature • Stone number • Stone length • Stone width Ureter N/A ANN 984 (688/296) Stone-free status three months after treatment through urinary tract plain x-ray and excretory urography 77.9% 75% 77.7% N/A Moorthy K 16 et al. (2016) • The mean of grey level matrix Kidney 10-20 ANN 120 (80/40) Stone-free status one month after treatment through non-contrast enhance computed tomography (NCCT) 80.7% 98.4% 90% N/A Seckiner I 3 et al. (2017) • Gender • No of stone • Location • Infundibulopelvic angle (IPA) • Primary/secondary nature of stone • Status of hydronephrosis • Age • Skin-to-stone distance • Stone density • Creatinine level Kidney N/A ANN 203 (171/32) Stone free status N/A N/A 88.7% N/A Mannil M 10 et al. (2018) • Three-dimensional texture analysis (3D-TA) Kidney 5-20 J48 Decision tree kNN ANN RF SMO 51 (34/17) Stone-free status 83 days after treatment by non-contrast abdominal CT scans 71% 53% 65% 71% 0.35 74% 68% 72% 74% 0.63 N/A 0.72 0.61 0.6 0.79 0.49 Choo MS 7 et al. (2018) • Age • Gender • Location • Length • Width • Stone Volume • Stone Length • Skin-to-stone distance • Stone Density • Creatinine • Urine specific gravity • pH • Urinalysis microscopic RBC Ureter 5.9 ± 2.3 DTA 791 (791/0) Stone-free status two weeks after treatment through CT scans or plain x-ray of kidneys, ureters, and bladder 0.96 0.86 0.92 0.951 Yang SW 9 et al. (2020) • Mean stone density • Stone volume • Skin-to-stone distance • Stone length • Psoas muscle cross-sectional area Kidney Ureter 9.2 ± 3.5 6.6 ± 1.5 RF XGBoost LightGBM 358 (286/72) Stone-free status (4,5 and 6 weeks after treatment, respectively) by non-contrast CT scans 0.74 0.75 0.78 0.92 0.93 0.92 0.86 0.87 0.88 0.85 0.84 0.85 Xu ZH 8 et al. (2021) • Stone length • Duration of diseased • Age • Stone width • pH Ureter N/A ANN 1083 (813/270) Stone-free status three months after treatment by KUB radiography N/A N/A 93.2% 0.935 Moghisi R 17 et al. (2022) • Stone location • Age • Kidney side • Electrode used • Stone treatment number • Number of shocks • Area of stone • Gender • BMI • Number of stones • Family history Kidney Ureter N/A AdaBoost 37013 (37013/0) Stone-free status three months after treatment 87.5% 65.3% 77.59% 0.843 Risk of bias This systematic review analyzes the accuracy of machine learning in predicting a condition; therefore, diagnostic articles are included. We use the Non-Proprietary Quality Assessment of Diagnostic Accuracy Studies APPRAISE-AI tool to assess the quality of the diagnostic studies. 18 Out of 11 included studies, 2 are moderate quality, 6 are high quality and 3 are very high. The risk of bias assessment is shown in Supplementary Table 1. Generally, the included studies are of moderate to good quality, only three of which have moderate quality. The risk of bias assessment is shown in Figure 3 . Figure 3. Risk of bias assessment using Cochrane Risk of Bias Assessment. Only two studies reported the use of additional procedures. Yang et al. performed an additional SWL session every one week if stones remained. 9 Postoperative stents, catheters, and contrast medium were used by Gomha et al. 15 In three articles, the absence of stones on plain abdominal radiographs determined stone-free status. 12 , 13 , 15 Two studies did not state their sampling method. 12 , 16 Artificial Intelligence/Machine Learning Accuracy in Predicting Stone-free Status A total of 9 MLs are observed in 11 studies, which are ANN, J48 decision tree, k-nearest neighbor (kNN), RF, Sequential Minimal Optimization (SMO), DTA, Extreme Gradient Boosting Trees (XGBoost), Light Gradient Boosting Method (LightGBM), and AdaBoost. 7 – 9 Eight out of 11 studies explored the diagnostic parameters of ANN. Most studies report that ANN has high specificity and high overall accuracy in predicting stone-free status. Four experiments out of 8 studies demonstrated ANN accuracy of at least 90%, with its accuracy ranging from 75-93%. It is followed by DTA with the accuracy of 92% and LightGBM with the accuracy of 88%. Discussion This review employed broad search criteria and inclusive inclusion criteria to ensure the inclusion of all relevant studies. Based on the Area Under the Curve (AUC) values, the ANN model presented by Michaels et al. 12 achieved the highest AUC. However, it’s noteworthy that this study utilized plain abdominal radiography as the reference standard instead of computerized urography. Interestingly, when plain abdominal radiography is used as the reference standard, the specificity tends to be higher, particularly when the stone is located outside the kidneys. Decision support systems like ANN serve as computer-generated algorithms aiding healthcare professionals in clinical decision-making. These algorithms, in various forms, aim to replicate the learning process of the human brain. They assist healthcare practitioners in making decisions based on specific clinical data from patients. By employing functions within ANN, computers are trained to predict specific parameters efficiently using training sets. Following training, the computer’s performance is evaluated using the data, assessing the extent of its learning in terms of validity and test data. If the computer demonstrates sufficient learning, it can make predictions for users. 3 ANN has the potential to greatly enhance the quality of healthcare services, facilitating early disease detection, reducing medical errors, and supporting healthcare authorities in cost-effective patient care. The decision-making process involves selecting one of several alternative outcomes generated by cognitive functions. With an increasing number of alternatives, the complexity of the decision-making process and the potential for errors also rise. At the conclusion of the decision-making process, there is an action or idea. Different formats can be employed to depict how individuals arrive at decisions. It is imperative to scrutinize the interplay between psychological elements, cognitive processes, and the surrounding context when assessing the decision-making process. Individuals are expected to generate specific recommendations through logical filtering and ultimately arrive at the correct decision. Given the value of time in decision-making, providing decision-makers with data as quickly as possible is essential for effective and rapid decision-making. Consequently, many administrative and specialized organizations currently rely on ANN for efficient and swift decision-making. 3 Machine learning models exhibit variable diagnostic accuracies, with sensitivity ranging from 35% to 96%, specificity from 63% to 98.4%, and AUC of ROC ranging from 0.49 to 0.96. In this study, it is demonstrated that the predictive accuracy of Random Forest (RF) and Decision Tree Analysis (DTA) in determining stone-free status surpasses that of ANN. It’s worth noting that the high AUC value of ANN was partly attributed to the use of plain abdominal radiography as a reference standard in some studies. Decision tree models present numerous benefits compared to current decision support tools. In contrast to decision tools relying on statistical approaches, artificial intelligence decision models adjust their operations based on the data as they are employed, allowing for the smooth incorporation of new data. Although this adaptability may pose overfitting challenges, setting a minimum number of cases can address this issue. Decision tree models can handle data with both quantitative and qualitative variables, making them versatile. Additionally, results are presented in a straightforward and interpretable manner, often in the form of a tree or a set of rules. Nonetheless, there are certain drawbacks to consider, like attribute importance metrics potentially exhibiting a bias toward variables possessing higher levels of data, including categorical variables with differing value counts. 7 RF, XGBoost, and LightGBM are three models based on DTA, explaining their comparable accuracy. 9 Information derived from the analysis of CT images consistently includes crucial predictive factors in the models, primarily mean stone density, stone volume, length, width, and three-dimensional texture analysis. These findings align with current guidelines. 19 Specifically, stone size, volume, length, or width are considered essential predictive factors in eight studies, while stone density is emphasized in three studies, and three-dimensional texture analysis is featured in one study. Incorporating these factors into the model enhances the predictive capability of machine learning for stone-free status. Several steps are involved in developing AI models. Some of the programs utilized in these studies include Alyuda NeuroIntelligence 2.2 for creating ANN and Quinlan C.50 for producing DTA. 3 , 7 The algorithm parameters were set to default values to identify factors with the highest predictive accuracy. In the case of DTA, the “minimal cases” parameter defaults to 2 and is used to restrict splits at nodes, leading to the creation of smaller trees as the value increases. By reducing splits at nodes based on this parameter, accuracy gradually decreases, preventing overfitting.7 During the creation of ANN, data were analyzed concerning training, validation, and testing. Both numerical and categorical data were used, and the percentage of data allocated to training, validation, or testing was determined. Subsequently, all data were converted into a numeric format for processing. The ANN structure was then formulated, with the number of neurons determined experimentally, as there are no specific rules in the literature for determining this. The logistic activation function was applied to all inputs and outputs in the ANN model, transforming values into the 0-1 range. 3 The RF model, developed by Mannil 10 et al., was created using open-source data mining software (WEKA, version 3.8.0; University of Waikato, Hamilton, New Zealand). A built-in feature selection filter was employed to assess individual features’ value and predictive contribution to SWL (Shock Wave Lithotripsy) success, considering both unique predictive value and correlation between features. 10 This systematic review has several limitations. Many included studies had small sample sizes in both training and testing sets, potentially misrepresenting the AI's true capabilities. The data were predominantly retrospective, introducing potential bias. Methodological issues, including reproducibility, the use of test datasets, diagnostic accuracy reporting, and lack of non-ML statistical comparisons, were also noted. The reproducibility of most studies is limited, with only one study providing model access. This limits the ability to evaluate models with different datasets. There is also significant variability in ESWL outcomes, highlighting the need for consensus to reduce heterogeneity. Furthermore, none of the studies compared their machine learning methods with clinical experts' evaluations, which could provide insights into the effectiveness of AI in clinical settings. Therefore, future studies should use bigger, prospective, and multicenter data that are externally validated with increased methodological transparency to develop better AI-models. Hybrid models by combining multiple ML approaches should also be explored to improve these existing models. Incorporating standardized reporting of ESWL outcomes predictors will made more progress to increase its clinical applicability. Conclusion In conclusion, ML can be used for predicting stone-free status in urinary stone diseases with satisfying accuracy however the accuracy of the prediction rely on many factors such as the ML model, variables taken into account and the data used for training set. The main advantage of ML in the prediction of stone free-status was it allows various factors to be taken into consideration even with the non-linear variables. Random forest method and DTA are superior MLs compared to ANN. Stone size, density, and 3D-texture analysis should be included in the models to ensure accuracy in predicting stone-free status after ESWL. However, access to the models should be made public, and further studies comparing them to the current statistical methods should be conducted. Data availability Underlying data Figshare: Artificial Intelligence or Machine learning for Prediction of stone-free status in Patients with urolithiasis.rm5, http://doi.org/10.6084/m9.figshare.27082798 . 20 The project contains the following underlying data: • Artificial Intelligence or Machine learning for Prediction of stone-free status in Patients with urolithiasis.rm5 (57.68 kB) Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Reporting guidelines Figshare: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review, DOI: http://doi.org/10.6084/m9.figshare.27173805 , 21 http://doi.org/10.6084/m9.figshare.27314595 22 The project contains the following Reporting guidelines data: • PRISMA Checklist • Prisma Flow Chart Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). 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Nakasato T, Morita J, Ogawa Y: Evaluation of Hounsfield Units as a predictive factor for the outcome of extracorporeal shock wave lithotripsy and stone composition. Urolithiasis. 2014; 43 : 69–75. Publisher Full Text 6. Yamashita S, Kohjimoto Y, Iwahashi Y, et al. : Noncontrast Computed Tomography Parameters for Predicting Shock Wave Lithotripsy Outcome in Upper Urinary Tract Stone Cases. Biomed. Res. Int. 2018; 2018 : 9253952. 7. Choo MS, Uhmn S, Kim JK, et al. : A Prediction Model Using Machine Learning Algorithm for Assessing Stone-Free Status after Single Session Shock Wave Lithotripsy to Treat Ureteral Stones. J. Urol. 2018; 200 (6): 1371–1377. PubMed Abstract | Publisher Full Text 8. Xu ZH, Zhou S, Jia CP, et al. : Prediction of Proximal Ureteral Stones Clearance after Shock Wave Lithotripsy Using an Artificial Neural Network. Urol. J. 2021; 18 (5): 491–496. PubMed Abstract | Publisher Full Text 9. Yang SW, Hyon YK, Na HS, et al. : Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy. BMC Urol. 2020; 20 (1): 88. PubMed Abstract | Publisher Full Text | Free Full Text 10. Mannil M, von Spiczak J , Hermanns T, et al. : Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones. J. Urol. 2018; 200 (4): 829–836. PubMed Abstract | Publisher Full Text 11. Sinha Y, Malviya P, Panda M, et al. : Contextual Care Protocol using Neural Networks and Decision Trees.2018; 1–4. 12. Michaels EK, Niederberger CS, Golden RM, et al. : Use of a neural network to predict stone growth after shock wave lithotripsy. Urology. 1998; 51 (2): 335–338. PubMed Abstract | Publisher Full Text 13. Poulakis V, Dahm P, Witzsch U, et al. : Prediction of lower pole stone clearance after shock wave lithotripsy using an artificial neural network. J. Urol. 2003; 169 (4): 1250–1256. PubMed Abstract | Publisher Full Text 14. Hamid A, Dwivedi US, Singh TN, et al. : Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study. BJU Int. 2003; 91 (9): 821–824. PubMed Abstract | Publisher Full Text 15. Gomha MA, Sheir KZ, Showky S, et al. : Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? A neural network or a statistical model? J. Urol. 2004; 172 (1): 175–179. Publisher Full Text 16. Moorthy K, Krishnan M: Prediction of fragmentation of kidney stones: A statistical approach from NCCT images. Can. Urol. Assoc. J. 2016; 10 (7-8): E237–E240. PubMed Abstract | Publisher Full Text 17. Moghisi R, El Morr C, Pace KT, et al. : A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study. Interact J. Med. Res. 2022; 11 (1): e33357. PubMed Abstract | Publisher Full Text | Free Full Text 18. Whiting PF, Rutjes AW, Westwood ME, et al. : QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 2011; 155 (8): 529–536. Publisher Full Text 19. Türk C, Petřík A, Sarica K, et al. : EAU Guidelines on Diagnosis and Conservative Management of Urolithiasis. Eur. Urol. 2016; 69 (3): 468–474. PubMed Abstract | Publisher Full Text 20. Ficky F, Rasyid N, Atmoko W, et al. : Artificial Intelligence or Machine learning for Prediction of stone-free status in Patients with urolithiasis.rm5.2024. Reference Source 21. Ficky F: Prisma 2020 Checklist for Artificial intelligence in the prediction of stone-free status in urinary stone disease treated with extracorporeal shockwave lithotripsy: A systematic review. Dataset. figshare. 2024. Publisher Full Text 22. Ficky F: Prisma 2020 Flowchart for Artificial intelligence in the prediction of stone-free status in urinary stone disease treated with extracorporeal shockwave lithotripsy: A systematic review. figshare. Figure. 2024. Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 03 Jan 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Urology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia Ficky . Roles: Conceptualization, Data Curation, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing Nur Rasyid Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Resources, Supervision Widi Atmoko Roles: Conceptualization, Data Curation, Investigation, Methodology, Software, Supervision, Writing – Review & Editing Ponco Birowo Roles: Conceptualization, Formal Analysis, Funding Acquisition, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 26 Mar 2025, 14:16 https://doi.org/10.12688/f1000research.152346.2 version 1 Published: 03 Jan 2025, 14:16 https://doi.org/10.12688/f1000research.152346.1 Copyright © 2025 . F et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article . F, Rasyid N, Atmoko W and Birowo P. Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.12688/f1000research.152346.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 26 Mar 2025 Revised Views 0 Cite How to cite this report: Chahar S. Reviewer Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.179593.r410676 ) The direct URL for this report is: https://f1000research.com/articles/14-16/v2#referee-response-410676 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 18 Sep 2025 Shriyansh Chahar , Wims Hospital, Agra, India, Agra,, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.179593.r410676 This systematic review examines the application of artificial intelligence (AI) and machine learning (ML) models in predicting stone-free outcomes following extracorporeal shockwave lithotripsy (ESWL). The authors conducted a comprehensive search across six databases, included 11 studies (41,484 patients), and assessed ... Continue reading READ ALL This systematic review examines the application of artificial intelligence (AI) and machine learning (ML) models in predicting stone-free outcomes following extracorporeal shockwave lithotripsy (ESWL). The authors conducted a comprehensive search across six databases, included 11 studies (41,484 patients), and assessed methodological quality using PRISMA, STARD, QUADAS-2, and APPRAISE-AI frameworks. The review highlights that Random Forest (RF) and Decision Tree Analysis (DTA) models outperform artificial neural networks (ANN) in prediction accuracy, and identifies stone size, density, and 3D texture analysis as key predictive variables. The study is timely, relevant, and well-structured. The authors have addressed most concerns raised in previous reviews, including updating the search, applying APPRAISE-AI, clarifying stone-free status definitions, and expanding the limitations section. A few points merit attention. First, the heterogeneity of included studies—differences in stone-free definitions, patient cohorts, and ML algorithms—should be emphasized more clearly, with consideration for subgroup analysis or at least a stronger acknowledgment of its impact on comparability. Second, while comparisons with statistical models are made, there is no benchmarking against clinician judgment; highlighting this gap would strengthen the clinical relevance. Third, the discussion on hybrid/ensemble approaches could be expanded, as combining ML models may improve predictive power. The limitations section could also be refined by distinguishing between weaknesses of the included studies (retrospective design, small datasets, lack of validation) and limitations of the review itself (language restriction, inability to perform meta-analysis). Lastly, simplifying Table 2 or adding a summary figure would improve readability. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: endourology, urolithiasis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Chahar S. Reviewer Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.179593.r410676 ) The direct URL for this report is: https://f1000research.com/articles/14-16/v2#referee-response-410676 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Barua R. Reviewer Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.179593.r373263 ) The direct URL for this report is: https://f1000research.com/articles/14-16/v2#referee-response-373263 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 31 Mar 2025 Ranjit Barua , Indian Institute of Engineering Science and Technology, Howrah, West bengal, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.179593.r373263 I have re-reviewed the manuscript. The authors ... Continue reading READ ALL I have re-reviewed the manuscript. The authors have modified the paper according to the suggestions. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Modern Robotics, MIS, Bioprinting I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Barua R. Reviewer Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.179593.r373263 ) The direct URL for this report is: https://f1000research.com/articles/14-16/v2#referee-response-373263 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 03 Jan 2025 Views 0 Cite How to cite this report: Barua R. Reviewer Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.167095.r361858 ) The direct URL for this report is: https://f1000research.com/articles/14-16/v1#referee-response-361858 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Jan 2025 Ranjit Barua , Indian Institute of Engineering Science and Technology, Howrah, West bengal, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.167095.r361858 Every scientific study has limitations, and acknowledging them strengthens its credibility. The current study should explicitly discuss potential drawbacks, such as small sample sizes, retrospective data analysis, and lack of external validation. Addressing these limitations would help guide future research ... Continue reading READ ALL Every scientific study has limitations, and acknowledging them strengthens its credibility. The current study should explicitly discuss potential drawbacks, such as small sample sizes, retrospective data analysis, and lack of external validation. Addressing these limitations would help guide future research in refining ML models for ESWL outcome prediction. The potential for hybrid models should also be explored. Combining multiple ML approaches, such as ensemble learning techniques that integrate RF and ANN, could improve predictive accuracy. Additionally, discussing the need for prospective, multi-center trials to validate ML models in real-world clinical settings would provide a clear roadmap for future research. The study presents valuable insights into the application of ML in predicting ESWL outcomes, but several areas require further refinement to enhance its scientific soundness. Methodological transparency, proper statistical validation, assessment of bias, and clinical applicability must be addressed to ensure the reliability and utility of ML models in urological practice. By implementing these recommendations, the study can contribute more effectively to the ongoing integration of ML into clinical decision-making for urolithiasis treatment. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Modern Robotics, MIS, Bioprinting I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Barua R. Reviewer Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.167095.r361858 ) The direct URL for this report is: https://f1000research.com/articles/14-16/v1#referee-response-361858 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 26 Mar 2025 Ficky Huang , Department of Urology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia 26 Mar 2025 Author Response 1. Acknowledging Limitations: We have acknowledged the limitations of small sample sizes, retrospective data, and lack of external validation. We’ve emphasized that future research should focus on larger, prospective, multi-center ... Continue reading 1. Acknowledging Limitations: We have acknowledged the limitations of small sample sizes, retrospective data, and lack of external validation. We’ve emphasized that future research should focus on larger, prospective, multi-center studies to better validate machine learning models. 2. Exploring Hybrid Models: In response to the suggestion, we’ve included the idea of exploring hybrid models combining Random Forest and Artificial Neural Networks (ANN). This could enhance predictive accuracy, and we plan to consider this approach in future studies. 3. Methodological Transparency and Clinical Applicability: We’ve agreed on the importance of transparency in methodology and comparing machine learning models with clinical experts’ evaluations. We highlighted the need for standardized outcome reporting and reducing variability in ESWL outcomes to improve clinical applicability. 1. Acknowledging Limitations: We have acknowledged the limitations of small sample sizes, retrospective data, and lack of external validation. We’ve emphasized that future research should focus on larger, prospective, multi-center studies to better validate machine learning models. 2. Exploring Hybrid Models: In response to the suggestion, we’ve included the idea of exploring hybrid models combining Random Forest and Artificial Neural Networks (ANN). This could enhance predictive accuracy, and we plan to consider this approach in future studies. 3. Methodological Transparency and Clinical Applicability: We’ve agreed on the importance of transparency in methodology and comparing machine learning models with clinical experts’ evaluations. We highlighted the need for standardized outcome reporting and reducing variability in ESWL outcomes to improve clinical applicability. Competing Interests: We have no competing interest Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 26 Mar 2025 Ficky Huang , Department of Urology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia 26 Mar 2025 Author Response 1. Acknowledging Limitations: We have acknowledged the limitations of small sample sizes, retrospective data, and lack of external validation. We’ve emphasized that future research should focus on larger, prospective, multi-center ... Continue reading 1. Acknowledging Limitations: We have acknowledged the limitations of small sample sizes, retrospective data, and lack of external validation. We’ve emphasized that future research should focus on larger, prospective, multi-center studies to better validate machine learning models. 2. Exploring Hybrid Models: In response to the suggestion, we’ve included the idea of exploring hybrid models combining Random Forest and Artificial Neural Networks (ANN). This could enhance predictive accuracy, and we plan to consider this approach in future studies. 3. Methodological Transparency and Clinical Applicability: We’ve agreed on the importance of transparency in methodology and comparing machine learning models with clinical experts’ evaluations. We highlighted the need for standardized outcome reporting and reducing variability in ESWL outcomes to improve clinical applicability. 1. Acknowledging Limitations: We have acknowledged the limitations of small sample sizes, retrospective data, and lack of external validation. We’ve emphasized that future research should focus on larger, prospective, multi-center studies to better validate machine learning models. 2. Exploring Hybrid Models: In response to the suggestion, we’ve included the idea of exploring hybrid models combining Random Forest and Artificial Neural Networks (ANN). This could enhance predictive accuracy, and we plan to consider this approach in future studies. 3. Methodological Transparency and Clinical Applicability: We’ve agreed on the importance of transparency in methodology and comparing machine learning models with clinical experts’ evaluations. We highlighted the need for standardized outcome reporting and reducing variability in ESWL outcomes to improve clinical applicability. Competing Interests: We have no competing interest Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Kwong JC. Reviewer Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.167095.r358874 ) The direct URL for this report is: https://f1000research.com/articles/14-16/v1#referee-response-358874 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 24 Jan 2025 Jethro CC Kwong , University of Toronto, Toronto, Canada Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.167095.r358874 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the past 2 years 2. This review can be strengthened ... Continue reading READ ALL 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the past 2 years 2. This review can be strengthened by examining the quality of the included ML studies using an AI-specific evaluation tool, such as APPRAISE-AI (Kwong JCC, et al. [2023-Ref -1] https://doi.org/10.1001/jamanetworkopen.2023.35377 ). It would be worthwhile to use this tool to identify specific methodological and reporting gaps in this specific area of study. 3. Did any of the studies compare the performance of the ML model against a non-ML approach (clinical judgement or other clinical tool)? If yes, what was the margin of difference? If not, this may be worthwhile to highlight as these studies did not demonstrate whether the ML approach outperforms traditional statistical approaches or clinical judgement 4. The authors should provide more detail regarding the stone-free status. From Table 2, it seems that there was heterogeneity in the timepoint at which stone-free status was determined, which can impact comparability of studies. Furthermore, additional details should be provided regarding determination of stone-free status (e.g. imaging modality, does stone-free mean complete absence of stone or was there a size cutoff of residual stone that was allowed). 5. The limitations of the included studies should be expanded. I think use of the APPRAISE-AI tool will help uncover additional limitations that are worth mentioning. This review can be strengthened if the authors can provide a list of recommendations on how future ML studies in this space can be improved, based on the limitations identified in the current review. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Partly Are the conclusions drawn adequately supported by the results presented in the review? Partly If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable References 1. Kwong JCC, Khondker A, Lajkosz K, McDermott MBA, et al.: APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. JAMA Netw Open . 2023; 6 (9): e2335377 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: AI in urology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Kwong JC. Reviewer Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.167095.r358874 ) The direct URL for this report is: https://f1000research.com/articles/14-16/v1#referee-response-358874 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 26 Mar 2025 Ficky Huang , Department of Urology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia 26 Mar 2025 Author Response 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the ... Continue reading 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the past 2 years Response: Updating the Search: We’ve decided to update the search to include studies published up to December 31, 2024, to ensure the review stays current with the latest advancements in machine learning for predicting stone-free status. 2. This review can be strengthened by examining the quality of the included ML studies using an AI-specific evaluation tool, such as APPRAISE-AI (Kwong JCC, et al. [2023-Ref -1] https://doi.org/10.1001/jamanetworkopen.2023.35377 ). It would be worthwhile to use this tool to identify specific methodological and reporting gaps in this specific area of study. Response: AI-Specific Evaluation Tool (APPRAISE-AI): We’ve recognized the value of using the APPRAISE-AI tool to assess the quality of machine learning studies. We have used APPRAISE-AI tool and made some changes. 3. Did any of the studies compare the performance of the ML model against a non-ML approach (clinical judgement or other clinical tool)? If yes, what was the margin of difference? If not, this may be worthwhile to highlight as these studies did not demonstrate whether the ML approach outperforms traditional statistical approaches or clinical judgement Response: Comparison with Non-ML Approaches: In response to the feedback, we will include a focus on comparing machine learning models with traditional methods, like clinical judgment, to evaluate their effectiveness in clinical practice. 4. The authors should provide more detail regarding the stone-free status. From Table 2, it seems that there was heterogeneity in the timepoint at which stone-free status was determined, which can impact comparability of studies. Furthermore, additional details should be provided regarding determination of stone-free status (e.g. imaging modality, does stone-free mean complete absence of stone or was there a size cutoff of residual stone that was allowed). Response: Clarification on Stone-Free Status: We’ve addressed the variability in defining stone-free status across studies and committed to providing more clarity on how this status was determined, including the imaging modalities used. 5. The limitations of the included studies should be expanded. I think use of the APPRAISE-AI tool will help uncover additional limitations that are worth mentioning. This review can be strengthened if the authors can provide a list of recommendations on how future ML studies in this space can be improved, based on the limitations identified in the current review. Response: Expanding the Limitations: We expanded the limitations section further and apply the APPRAISE-AI tool to uncover additional issues, with recommendations for improving future machine learning studies. 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the past 2 years Response: Updating the Search: We’ve decided to update the search to include studies published up to December 31, 2024, to ensure the review stays current with the latest advancements in machine learning for predicting stone-free status. 2. This review can be strengthened by examining the quality of the included ML studies using an AI-specific evaluation tool, such as APPRAISE-AI (Kwong JCC, et al. [2023-Ref -1] https://doi.org/10.1001/jamanetworkopen.2023.35377 ). It would be worthwhile to use this tool to identify specific methodological and reporting gaps in this specific area of study. Response: AI-Specific Evaluation Tool (APPRAISE-AI): We’ve recognized the value of using the APPRAISE-AI tool to assess the quality of machine learning studies. We have used APPRAISE-AI tool and made some changes. 3. Did any of the studies compare the performance of the ML model against a non-ML approach (clinical judgement or other clinical tool)? If yes, what was the margin of difference? If not, this may be worthwhile to highlight as these studies did not demonstrate whether the ML approach outperforms traditional statistical approaches or clinical judgement Response: Comparison with Non-ML Approaches: In response to the feedback, we will include a focus on comparing machine learning models with traditional methods, like clinical judgment, to evaluate their effectiveness in clinical practice. 4. The authors should provide more detail regarding the stone-free status. From Table 2, it seems that there was heterogeneity in the timepoint at which stone-free status was determined, which can impact comparability of studies. Furthermore, additional details should be provided regarding determination of stone-free status (e.g. imaging modality, does stone-free mean complete absence of stone or was there a size cutoff of residual stone that was allowed). Response: Clarification on Stone-Free Status: We’ve addressed the variability in defining stone-free status across studies and committed to providing more clarity on how this status was determined, including the imaging modalities used. 5. The limitations of the included studies should be expanded. I think use of the APPRAISE-AI tool will help uncover additional limitations that are worth mentioning. This review can be strengthened if the authors can provide a list of recommendations on how future ML studies in this space can be improved, based on the limitations identified in the current review. Response: Expanding the Limitations: We expanded the limitations section further and apply the APPRAISE-AI tool to uncover additional issues, with recommendations for improving future machine learning studies. Competing Interests: We have no Competing Interests Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 26 Mar 2025 Ficky Huang , Department of Urology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia 26 Mar 2025 Author Response 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the ... Continue reading 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the past 2 years Response: Updating the Search: We’ve decided to update the search to include studies published up to December 31, 2024, to ensure the review stays current with the latest advancements in machine learning for predicting stone-free status. 2. This review can be strengthened by examining the quality of the included ML studies using an AI-specific evaluation tool, such as APPRAISE-AI (Kwong JCC, et al. [2023-Ref -1] https://doi.org/10.1001/jamanetworkopen.2023.35377 ). It would be worthwhile to use this tool to identify specific methodological and reporting gaps in this specific area of study. Response: AI-Specific Evaluation Tool (APPRAISE-AI): We’ve recognized the value of using the APPRAISE-AI tool to assess the quality of machine learning studies. We have used APPRAISE-AI tool and made some changes. 3. Did any of the studies compare the performance of the ML model against a non-ML approach (clinical judgement or other clinical tool)? If yes, what was the margin of difference? If not, this may be worthwhile to highlight as these studies did not demonstrate whether the ML approach outperforms traditional statistical approaches or clinical judgement Response: Comparison with Non-ML Approaches: In response to the feedback, we will include a focus on comparing machine learning models with traditional methods, like clinical judgment, to evaluate their effectiveness in clinical practice. 4. The authors should provide more detail regarding the stone-free status. From Table 2, it seems that there was heterogeneity in the timepoint at which stone-free status was determined, which can impact comparability of studies. Furthermore, additional details should be provided regarding determination of stone-free status (e.g. imaging modality, does stone-free mean complete absence of stone or was there a size cutoff of residual stone that was allowed). Response: Clarification on Stone-Free Status: We’ve addressed the variability in defining stone-free status across studies and committed to providing more clarity on how this status was determined, including the imaging modalities used. 5. The limitations of the included studies should be expanded. I think use of the APPRAISE-AI tool will help uncover additional limitations that are worth mentioning. This review can be strengthened if the authors can provide a list of recommendations on how future ML studies in this space can be improved, based on the limitations identified in the current review. Response: Expanding the Limitations: We expanded the limitations section further and apply the APPRAISE-AI tool to uncover additional issues, with recommendations for improving future machine learning studies. 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the past 2 years Response: Updating the Search: We’ve decided to update the search to include studies published up to December 31, 2024, to ensure the review stays current with the latest advancements in machine learning for predicting stone-free status. 2. This review can be strengthened by examining the quality of the included ML studies using an AI-specific evaluation tool, such as APPRAISE-AI (Kwong JCC, et al. [2023-Ref -1] https://doi.org/10.1001/jamanetworkopen.2023.35377 ). It would be worthwhile to use this tool to identify specific methodological and reporting gaps in this specific area of study. Response: AI-Specific Evaluation Tool (APPRAISE-AI): We’ve recognized the value of using the APPRAISE-AI tool to assess the quality of machine learning studies. We have used APPRAISE-AI tool and made some changes. 3. Did any of the studies compare the performance of the ML model against a non-ML approach (clinical judgement or other clinical tool)? If yes, what was the margin of difference? If not, this may be worthwhile to highlight as these studies did not demonstrate whether the ML approach outperforms traditional statistical approaches or clinical judgement Response: Comparison with Non-ML Approaches: In response to the feedback, we will include a focus on comparing machine learning models with traditional methods, like clinical judgment, to evaluate their effectiveness in clinical practice. 4. The authors should provide more detail regarding the stone-free status. From Table 2, it seems that there was heterogeneity in the timepoint at which stone-free status was determined, which can impact comparability of studies. Furthermore, additional details should be provided regarding determination of stone-free status (e.g. imaging modality, does stone-free mean complete absence of stone or was there a size cutoff of residual stone that was allowed). Response: Clarification on Stone-Free Status: We’ve addressed the variability in defining stone-free status across studies and committed to providing more clarity on how this status was determined, including the imaging modalities used. 5. The limitations of the included studies should be expanded. I think use of the APPRAISE-AI tool will help uncover additional limitations that are worth mentioning. This review can be strengthened if the authors can provide a list of recommendations on how future ML studies in this space can be improved, based on the limitations identified in the current review. Response: Expanding the Limitations: We expanded the limitations section further and apply the APPRAISE-AI tool to uncover additional issues, with recommendations for improving future machine learning studies. Competing Interests: We have no Competing Interests Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 03 Jan 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 26 Mar 25 read read Version 1 03 Jan 25 read read Jethro CC Kwong , University of Toronto, Toronto, Canada Ranjit Barua , Indian Institute of Engineering Science and Technology, Howrah, India Shriyansh Chahar , Wims Hospital, Agra, India, Agra,, India Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Chahar S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 18 Sep 2025 | for Version 2 Shriyansh Chahar , Wims Hospital, Agra, India, Agra,, India 0 Views copyright © 2025 Chahar S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This systematic review examines the application of artificial intelligence (AI) and machine learning (ML) models in predicting stone-free outcomes following extracorporeal shockwave lithotripsy (ESWL). The authors conducted a comprehensive search across six databases, included 11 studies (41,484 patients), and assessed methodological quality using PRISMA, STARD, QUADAS-2, and APPRAISE-AI frameworks. The review highlights that Random Forest (RF) and Decision Tree Analysis (DTA) models outperform artificial neural networks (ANN) in prediction accuracy, and identifies stone size, density, and 3D texture analysis as key predictive variables. The study is timely, relevant, and well-structured. The authors have addressed most concerns raised in previous reviews, including updating the search, applying APPRAISE-AI, clarifying stone-free status definitions, and expanding the limitations section. A few points merit attention. First, the heterogeneity of included studies—differences in stone-free definitions, patient cohorts, and ML algorithms—should be emphasized more clearly, with consideration for subgroup analysis or at least a stronger acknowledgment of its impact on comparability. Second, while comparisons with statistical models are made, there is no benchmarking against clinician judgment; highlighting this gap would strengthen the clinical relevance. Third, the discussion on hybrid/ensemble approaches could be expanded, as combining ML models may improve predictive power. The limitations section could also be refined by distinguishing between weaknesses of the included studies (retrospective design, small datasets, lack of validation) and limitations of the review itself (language restriction, inability to perform meta-analysis). Lastly, simplifying Table 2 or adding a summary figure would improve readability. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes Competing Interests No competing interests were disclosed. Reviewer Expertise endourology, urolithiasis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Chahar S. Peer Review Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.179593.r410676) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-16/v2#referee-response-410676 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Barua R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 31 Mar 2025 | for Version 2 Ranjit Barua , Indian Institute of Engineering Science and Technology, Howrah, West bengal, India 0 Views copyright © 2025 Barua R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I have re-reviewed the manuscript. The authors have modified the paper according to the suggestions. Competing Interests No competing interests were disclosed. Reviewer Expertise Modern Robotics, MIS, Bioprinting I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Barua R. Peer Review Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.179593.r373263) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-16/v2#referee-response-373263 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Barua R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Jan 2025 | for Version 1 Ranjit Barua , Indian Institute of Engineering Science and Technology, Howrah, West bengal, India 0 Views copyright © 2025 Barua R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Every scientific study has limitations, and acknowledging them strengthens its credibility. The current study should explicitly discuss potential drawbacks, such as small sample sizes, retrospective data analysis, and lack of external validation. Addressing these limitations would help guide future research in refining ML models for ESWL outcome prediction. The potential for hybrid models should also be explored. Combining multiple ML approaches, such as ensemble learning techniques that integrate RF and ANN, could improve predictive accuracy. Additionally, discussing the need for prospective, multi-center trials to validate ML models in real-world clinical settings would provide a clear roadmap for future research. The study presents valuable insights into the application of ML in predicting ESWL outcomes, but several areas require further refinement to enhance its scientific soundness. Methodological transparency, proper statistical validation, assessment of bias, and clinical applicability must be addressed to ensure the reliability and utility of ML models in urological practice. By implementing these recommendations, the study can contribute more effectively to the ongoing integration of ML into clinical decision-making for urolithiasis treatment. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Modern Robotics, MIS, Bioprinting I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 26 Mar 2025 Ficky Huang, Department of Urology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia 1. Acknowledging Limitations: We have acknowledged the limitations of small sample sizes, retrospective data, and lack of external validation. We’ve emphasized that future research should focus on larger, prospective, multi-center studies to better validate machine learning models. 2. Exploring Hybrid Models: In response to the suggestion, we’ve included the idea of exploring hybrid models combining Random Forest and Artificial Neural Networks (ANN). This could enhance predictive accuracy, and we plan to consider this approach in future studies. 3. Methodological Transparency and Clinical Applicability: We’ve agreed on the importance of transparency in methodology and comparing machine learning models with clinical experts’ evaluations. We highlighted the need for standardized outcome reporting and reducing variability in ESWL outcomes to improve clinical applicability. View more View less Competing Interests We have no competing interest reply Respond Report a concern Barua R. Peer Review Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.167095.r361858) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-16/v1#referee-response-361858 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Kwong J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Jan 2025 | for Version 1 Jethro CC Kwong , University of Toronto, Toronto, Canada 0 Views copyright © 2025 Kwong J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the past 2 years 2. This review can be strengthened by examining the quality of the included ML studies using an AI-specific evaluation tool, such as APPRAISE-AI (Kwong JCC, et al. [2023-Ref -1] https://doi.org/10.1001/jamanetworkopen.2023.35377 ). It would be worthwhile to use this tool to identify specific methodological and reporting gaps in this specific area of study. 3. Did any of the studies compare the performance of the ML model against a non-ML approach (clinical judgement or other clinical tool)? If yes, what was the margin of difference? If not, this may be worthwhile to highlight as these studies did not demonstrate whether the ML approach outperforms traditional statistical approaches or clinical judgement 4. The authors should provide more detail regarding the stone-free status. From Table 2, it seems that there was heterogeneity in the timepoint at which stone-free status was determined, which can impact comparability of studies. Furthermore, additional details should be provided regarding determination of stone-free status (e.g. imaging modality, does stone-free mean complete absence of stone or was there a size cutoff of residual stone that was allowed). 5. The limitations of the included studies should be expanded. I think use of the APPRAISE-AI tool will help uncover additional limitations that are worth mentioning. This review can be strengthened if the authors can provide a list of recommendations on how future ML studies in this space can be improved, based on the limitations identified in the current review. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Partly Are the conclusions drawn adequately supported by the results presented in the review? Partly If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable References 1. Kwong JCC, Khondker A, Lajkosz K, McDermott MBA, et al.: APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. JAMA Netw Open . 2023; 6 (9): e2335377 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise AI in urology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 26 Mar 2025 Ficky Huang, Department of Urology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia 1. The search was conducted on Dec 12, 2022. I would recommend updating the search to at least Dec 31, 2024 as there may be newer models published in the past 2 years Response: Updating the Search: We’ve decided to update the search to include studies published up to December 31, 2024, to ensure the review stays current with the latest advancements in machine learning for predicting stone-free status. 2. This review can be strengthened by examining the quality of the included ML studies using an AI-specific evaluation tool, such as APPRAISE-AI (Kwong JCC, et al. [2023-Ref -1] https://doi.org/10.1001/jamanetworkopen.2023.35377 ). It would be worthwhile to use this tool to identify specific methodological and reporting gaps in this specific area of study. Response: AI-Specific Evaluation Tool (APPRAISE-AI): We’ve recognized the value of using the APPRAISE-AI tool to assess the quality of machine learning studies. We have used APPRAISE-AI tool and made some changes. 3. Did any of the studies compare the performance of the ML model against a non-ML approach (clinical judgement or other clinical tool)? If yes, what was the margin of difference? If not, this may be worthwhile to highlight as these studies did not demonstrate whether the ML approach outperforms traditional statistical approaches or clinical judgement Response: Comparison with Non-ML Approaches: In response to the feedback, we will include a focus on comparing machine learning models with traditional methods, like clinical judgment, to evaluate their effectiveness in clinical practice. 4. The authors should provide more detail regarding the stone-free status. From Table 2, it seems that there was heterogeneity in the timepoint at which stone-free status was determined, which can impact comparability of studies. Furthermore, additional details should be provided regarding determination of stone-free status (e.g. imaging modality, does stone-free mean complete absence of stone or was there a size cutoff of residual stone that was allowed). Response: Clarification on Stone-Free Status: We’ve addressed the variability in defining stone-free status across studies and committed to providing more clarity on how this status was determined, including the imaging modalities used. 5. The limitations of the included studies should be expanded. I think use of the APPRAISE-AI tool will help uncover additional limitations that are worth mentioning. This review can be strengthened if the authors can provide a list of recommendations on how future ML studies in this space can be improved, based on the limitations identified in the current review. Response: Expanding the Limitations: We expanded the limitations section further and apply the APPRAISE-AI tool to uncover additional issues, with recommendations for improving future machine learning studies. View more View less Competing Interests We have no Competing Interests reply Respond Report a concern Kwong JC. Peer Review Report For: Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 14 :16 ( https://doi.org/10.5256/f1000research.167095.r358874) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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