Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB

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Under the immense burden of radiology practice, Artificial Intelligence (AI) assists in obtaining Computed Tomography (CT) images with less scan time, proper patient placement, low radiation dose (RD), and improved image quality (IQ). Hence, the aim of this study was to evaluate and compare the positioning accuracy, RD, and IQ of AI-based automatic and manual positioning techniques for CT kidney ureters and bladder (CT KUB). Methods This prospective study included 143 patients in each group who were referred for computed tomography (CT) KUB examination. Group 1 patients underwent manual positioning (MP), and group 2 patients underwent AI-based automatic positioning (AP) for CT KUB examination. The scanning protocol was kept constant for both the groups. The off-center distance, RD, and quantitative and qualitative IQ of each group were evaluated and compared. Results The AP group (9.66±6.361 mm) had significantly less patient off-center distance than the MP group (15.12±9.55 mm). There was a significant reduction in RD in the AP group compared with that in the MP group. The quantitative image noise (IN) was lower, with a higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the AP group than in the MP group (p<0.05). Qualitative IQ parameters such as IN, sharpness, and overall IQ also showed significant differences (p< 0.05), with higher scores in the AP group than in the MP group. Conclusions The AI-based AP showed higher positioning accuracy with less off-center distance (44%), which resulted in 12% reduction in RD and improved IQ for CT KUB imaging compared with MP. 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F1000Research 2024, 13 :683 ( https://doi.org/10.12688/f1000research.150779.2 ) 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 ▬ ✚ Research Article Revised Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] Souradip Kundu 1 , Kaushik Nayak 1 , Rajagopal Kadavigere https://orcid.org/0000-0003-3486-8740 2 , Saikiran Pendem https://orcid.org/0000-0001-7933-1192 1 , Priyanka . https://orcid.org/0000-0002-9792-6242 1 Souradip Kundu 1 , Kaushik Nayak 1 , [...] Rajagopal Kadavigere https://orcid.org/0000-0003-3486-8740 2 , Saikiran Pendem https://orcid.org/0000-0001-7933-1192 1 , Priyanka . https://orcid.org/0000-0002-9792-6242 1 PUBLISHED 04 Jul 2024 Author details Author details 1 Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India 2 Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India Souradip Kundu Roles: Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation Kaushik Nayak Roles: Data Curation, Formal Analysis, Investigation, Supervision, Writing – Original Draft Preparation Rajagopal Kadavigere Roles: Formal Analysis, Investigation, Methodology, Supervision, Validation, Writing – Review & Editing Saikiran Pendem Roles: Investigation, Methodology, Validation, Visualization, Writing – Review & Editing Priyanka . Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Manipal Academy of Higher Education gateway. Abstract Background Recent innovations are making radiology more advanced for patient and patient services. Under the immense burden of radiology practice, Artificial Intelligence (AI) assists in obtaining Computed Tomography (CT) images with less scan time, proper patient placement, low radiation dose (RD), and improved image quality (IQ). Hence, the aim of this study was to evaluate and compare the positioning accuracy, RD, and IQ of AI-based automatic and manual positioning techniques for CT kidney ureters and bladder (CT KUB). Methods This prospective study included 143 patients in each group who were referred for computed tomography (CT) KUB examination. Group 1 patients underwent manual positioning (MP), and group 2 patients underwent AI-based automatic positioning (AP) for CT KUB examination. The scanning protocol was kept constant for both the groups. The off-center distance, RD, and quantitative and qualitative IQ of each group were evaluated and compared. Results The AP group (9.66±6.361 mm) had significantly less patient off-center distance than the MP group (15.12±9.55 mm). There was a significant reduction in RD in the AP group compared with that in the MP group. The quantitative image noise (IN) was lower, with a higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the AP group than in the MP group (p<0.05). Qualitative IQ parameters such as IN, sharpness, and overall IQ also showed significant differences (p< 0.05), with higher scores in the AP group than in the MP group. Conclusions The AI-based AP showed higher positioning accuracy with less off-center distance (44%), which resulted in 12% reduction in RD and improved IQ for CT KUB imaging compared with MP. READ ALL READ LESS Keywords Artificial Intelligence, Computed Tomography, Image quality, Positioning accuracy, Radiation dose Corresponding Author(s) Priyanka . ( [email protected] ) Close Corresponding author: Priyanka . Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Kundu S 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: Kundu S, Nayak K, Kadavigere R et al. Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.12688/f1000research.150779.2 ) First published: 24 Jun 2024, 13 :683 ( https://doi.org/10.12688/f1000research.150779.1 ) Latest published: 04 Jul 2024, 13 :683 ( https://doi.org/10.12688/f1000research.150779.2 ) Revised Amendments from Version 1 In the Result section, the reviewer asked to correct the typographic errors in the unit of off-center distance as millimeter. The same is corrected in the Result section. In the Result section, the reviewer asked to correct the typographic errors in the unit of off-center distance as millimeter. The same is corrected in the Result section. To read any peer review reports and author responses for this article, follow the "read" links in the Open Peer Review table. READ REVIEWER RESPONSES Introduction Computed Tomography (CT) is a valuable imaging modality for the diagnosis of various pathologies. However, CT scans use X-rays, which involve exposure to ionizing radiation. Therefore, the radiation exposure in CT must be kept “As Low as Reasonably Achievable (ALARA)”. 1 – 3 In recent years, several dose optimization tools have been introduced, such as “deep learning image reconstruction (DLIR)”, “iterative reconstruction (IR)”, “automatic tube current modulation (ATCM),” and “automatic tube voltage selection (ATVS)”. 4 – 9 In addition, proper patient positioning is crucial for obtaining higher image quality with an optimized radiation dose. 10 Radiology medical technologists can utilize laser lights to visually evaluate the patient’s central placement in CT imaging; however, this approach is user-dependent, and patient miscentering is common and well-documented problem that can have detrimental consequences. 11 If the patient is placed away from the gantry isocenter (i.e., the table is too up or down), the localizer image will be either enlarged or reduced in width. Furthermore, the use of ATCM along with patient miscentering could lead to an unacceptable Image quality (IQ) with an increase in the radiation dose (RD). 12 – 14 For manual positioning, the interaction between the radiographers and the patient poses a risk of cross-infection in patients with infectious diseases. 14 Recently, an artificial intelligence (AI)-based positioning camera, which works based on an AI algorithm that uses intelligence (a body contour detection algorithm) to detect a patient’s body using a three-dimensional (3D) camera. 15 Various companies have introduced AI-based contactless positions for patients. A 3D camera equipped with a visible light camera, an infrared light source, and a sensor was installed above the patient. It adjusts the height of the table and maintains the patient within the isocenter of the gantry. It also detects body contours and automatically positions the patient for CT examination according to the selected protocol. 14 – 18 Hence, this study aimed to evaluate and compare the positioning accuracy, IQ, and RD of AI-based automatic and manual positioning for CT Kidney Ureter and Bladder (KUB) imaging. Methods Study design This is a prospective study. Ethical approval was obtained from the Institutional ethical committee (IEC 168/2023) of Kasturba Medical College and Hospital, Manipal, India on 7 th June 2023, and then the study was registered on Clinical Trial Registry- India (CTRI/2023/06/054173) on 20 th June 2023. Written informed consent was obtained from all participants for publication and participation in the data collection for the study. Eligibility criteria The study included a total of 286 patients, with 143 patients in each group referred for CT KUB Imaging for various clinical indications such as evaluation of renal calculi, flank pain, kidney masses, and traumatic injury to the kidneys. Patients who were uncooperative and those with CT KUB images with artifacts (movement and metal) were excluded. Patient age and BMI were noted, and only patients with normal BMI were included. All patients underwent CT using a Philips Incisive 128 Slice CT Scanner. Patient positioning Patients in group 1 Manual positioning (MP group) underwent CT KUB imaging using manual positioning. The patient was positioned supine on a scan table with the feet first towards the gantry, and the arms were extended and supported above the head. The table height was adjusted by the gantry-mounted adjustment button such that the horizontal laser beam coincided with the mid-coronal plane of the patient and to the gantry isocenter by visual inspection. The area covered the dome of the diaphragm immediately below the symphysis pubis. Group 2 Automatic Positioning (AP group) patients underwent CT KUB imaging by AI-based automatic patient positioning, which included an AI-enabled camera mounted on the ceiling above the patient table. An AI-based camera automatically detected the patient’s orientation in the supine position with the feet first into the gantry. After selecting the CT KUB protocol, the area of interest (from the diaphragm to the symphysis pubis) to be scanned was automatically detected using an AI – based camera, and the table height was adjusted to the gantry isocenter. Image acquisition The image acquisition parameters were kept the same for both groups such as use of ATCM, tube voltage of 120 kVp, rotation time of 0.75s, pitch 1.0, matrix 512*512, slice thickness and increment 3 mm. The MP group patient images was reconstructed with IR technique – iDose 4 - level 4 (Philips Health Care ® ,TM ). The AP group patient images were reconstructed with DLIR technique (Precise Image; Philips Health Care ® ,TM ). The axial CT images from both the groups were reconstructed to extended field of view (FOV) of 500 mm. Off-center distance measurement The off-center distance was measured to evaluate the accuracy of patient positioning. To calculate the off-center distance, an axial slice of the CT KUB image at the level of the fourth lumbar vertebra, with an Field of View (FOV) of 500 mm, was selected. A straight line was drawn that joins the anterior and posterior margins of the complete FOV, and the midpoint of this line was determined, which represents the gantry isocenter. Another straight line that joined the anterior and posterior surfaces of the patient was drawn, and the midpoint of this line was determined to represent the patient’s center. The distance between the gantry isocenter and the patient’s center was measured using a measuring tool to evaluate the off-center distance 12 ( Figure 1 ). The scan length was noted in both the groups. Figure 1. Off-center distance measurement. Radiation dose measurement Radiation dose descriptors such as “Volumetric Computed Tomography Dose Index (CTDIv) in mGy,” “Dose Length Product (DLP) in mGy.cm”, “Size Specific Dose Estimate (SSDE) in mGy” was noted from the CT scanner and the “effective dose (ED)” was calculated using the following formula: E = DLP X Conversion factor ( K ) (K= 0.015 mSv/mGy. cm). 19 Quantitative image quality Quantitative IQ was assessed by calculating “signal to noise ratio (SNR),” “contrast to noise ratio (CNR)” and “image noise (IN).” 3 mm slice thickness axial CT KUB images were selected, and six circular regions of interest (ROI) measuring 4-5 mm 2 in diameter were drawn in the following regions: upper poles of the kidneys, lower poles, subcutaneous fat, and psoas muscle ( Figure 2A and 2B ). Figure 2. For quantitative assessment of image quality, A shows ROI were placed in upper (ROI 1) and lower pole (ROI 2) of right kidney, upper (ROI 3) and lower pole (ROI 4) of left kidney subcutaneous fat (ROI 5). B shows ROI placed in psoas muscle (ROI 6). The IN was calculated as the standard deviation (SD) of the ROI, and the attenuation value was the mean value of the ROI. SNR and CNR were calculated using the following formula 20 : SNR = Attenuation value Image Noise CNR = CT Kidney attenuation value – CTPsoas muscle attenuation value Total image noise Qualitative analysis Qualitative analysis of IQ was performed by two radiologists with > 10 years of experience in CT KUB reporting who were blinded to the patient positioning and image reconstruction technique. They assessed “image noise (IN)”, “image sharpness (IS)”, “image artifacts (IA)”, and “overall image quality (OIQ)” using a five-point Likert scale, as shown in Figure 3 . Figure 3. Qualitative analysis of image quality. Statistical analysis Statistical analysis of the data was performed using Jamovi -2.3.28.0 ( https://www.jamovi.org/download.html ). An independent t-test was used to compare RD (CTDIvol, DLP, SSDE, and ED), scan length, quantitative IQ (attenuation, image noise, SNR, and CNR), and off-center distance between the MP and AP positioning groups. The Chi-square test was used to compare the qualitative analysis of IQ. Statistical significance was set at p< 0.05. Kappa value (k-value) was calculated to assess the interobserver agreement of qualitative IQ analysis and the scores as follows: almost perfect (0.81-0.99)”, “substantial (0.60 – 0.79)”, “moderate (0.40 – 0.59)”, “fair (0.21 – 0.39), “and “< 0.20, none to slight agreement”. 21 Results The study included 286 patients referred for CT KUB imaging, 143 patients underwent CT KUB imaging using manual positioning, and the remaining 143 patients underwent automatic positioning. Patient details are summarized in Table 1 . Table 1. Summary of patient details. Demographic data MP group AP group Gender, male: female 101:42 93:50 Age, mean (SD) (years) 46 (16.20) 44 (15.61) Height, mean (SD) (cm) 168.7 (5.14) 170.14 (5.10) Weight, mean (SD) (kg) 63.64 (5.69) 61.16 (6.09) BMI, mean (SD) 22.36 (1.82) 21.12 (1.87) Off-center distance The mean off-center distance in the MP group and AP group was 15.12 ± 9.55 mm and 9.66 ± 6.361 mm. A statistically significant difference in the off-center distance (p < 0.05) was noted between the MP and AP group. The AP group showed 44% less off-center distance compared to the MP group. Scan length also showed a significant difference (p < 0.05) between the AP (56.0 ± 1.75 cm) and MP group (58.2 ± 3.55 cm). Radiation dose The mean and standard deviation (SD) of the radiation dose indices for both groups are shown in Table 2 . There was a statistically significant difference in the measured CTDIvol (p < 0.05), DLP (p < 0.05), SSDE (p < 0.05), and effective dose (p < 0.05) between the MP and AP groups. The AI based AP group showed 8.38%, 12.32%, 10.32%, 12.42% reductions in CTDIvol, DLP, SSDE, and ED, respectively, compared with MP group. Table 2. Comparison of radiation dose indices between MP group and AP group. Radiation dose Indices MP group Mean (SD) AP group Mean (SD) p – value CTDIvol (mGy) 8.207 (0.905) 7.541 (0.943) <0.05 DLP (mGy.cm) 478.783 (67.111) 423.181 (63.529) <0.05 SSDE (mGy) 10.081 (0.635) 9.092 (0.436) <0.05 Effective dose (mSv) 7.182 (1.006) 6.348 (0.953) <0.05 Quantitative IQ The mean and SD of the quantitative IQ parameters for both groups are shown in Table 3 . Quantitative IQ parameters, such as attenuation of the right kidney (p = 0.740), left kidney (p = 0.570), psoas muscle (p = 0.157), and subcutaneous fat (p = 0.053), did not show significant differences between the MP and AP group. However, other parameters such as IN and SNR of the right kidney, left kidney, psoas muscle, and subcutaneous fat showed statistically significant differences (p < 0.05), with lower IN and higher SNR in the AP group than in the MP group. The AP group showed 46.42% total IN reduction compared to MP group Similarly, the CNR of right and left kidney was higher in AP group compared MP group with significant difference (p < 0.05). Table 3. Quantitative IQ analysis between MP and AP group. Quantitative IQ MP group Mean (SD) AP group Mean (SD) p-value Attenuation (HU) Right Kidney 29.059 (4.0) 28.83 (3.51) 0.740 Left Kidney 28.75 (3.90) 28.48 (3.39) 0.570 Psoas muscle 49.80 (5.33) 50.59 (5.37) 0.157 Subcutaneous fat 110.96 (7.68) 112.47 (6.16) 0.053 Image noise (IN) Right Kidney 13.35 (3.81) 8.03(1.97) <0.05 Left Kidney 13.13 (3.75) 8.11(1.95) <0.05 Psoas muscle 14.71 (3.93) 9.41 (2.19) <0.05 Subcutaneous fat 10.26(3.32) 6.55 (2.33) <0.05 Total IN 51.54 (11.02) 32.12 (6.44) <0.05 Signal to noise ratio (SNR) Right Kidney 2.33 (0.71) 3.80 (1.03) <0.05 Left Kidney 2.34 (0.70) 3.73(1.05) <0.05 Psoas muscle 3.61 (1.01) 5.74 (1.81) <0.05 Subcutaneous fat 12.31 (5.51) 19.08 (6.11) <0.05 Contrast to noise ratio (CNR) Right Kidney 0.41 (0.14) 0.70 (08.24) <0.05 Left Kidney 0.42 (0.14) 0.71 (0.25) <0.05 Qualitative IQ The qualitative IQ scores of both readers in the MP and AP groups are shown in Table 4 . IN, IS, and OIQ showed a statistically significant difference (p < 0.05) between the two groups, with higher scores in the AP group than in the MP group for both readers ( Figure 4 ). There was no significant difference in IA scores (p = 0.652) between the MP and AP group. However, none of the images were rated as suboptimal or unacceptable (score < 4) by the two readers. IN (MP, k = 0.98; AP, k = 0.88), IA (MP and AP, k = 1), IS (MP, k = 0.97; AP, k = 0.92), and OIQ (MP, k = 0.97; AP, k = 0.94) showed almost perfect inter-observer agreement between the two readers. Table 4. Qualitative IQ analysis between Manual and Automatic positioning group. Qualitative IQ R1 R2 k-value p-value (MP vs AP) Scores 1 2 3 4 5 Mean (SD) 1 2 3 4 5 Mean (SD) R1 R2 MP group IN 0 0 0 128 15 4.10 (0.31) 0 0 0 127 16 4.11 (0.32) 0.98 <0.05 <0.05 IA 0 0 0 03 140 4.98 (0.15) 0 0 0 03 140 4.98 (0.15) 1.00 0.652 0.652 IS 0 0 0 20 123 4.86 (0.34) 0 0 0 20 123 4.86 (0.35) 0.97 <0.05 <0.05 OIQ 0 0 0 132 11 4.08 (0.26) 0 0 0 131 12 4.08 (0.28) 0.97 <0.05 <0.05 AP group IN 0 0 0 03 140 4.98 (0.15) 0 0 0 02 141 4.99 (0.12) 0.88 - - IA 0 0 0 02 141 4.99 (0.12) 0 0 0 02 141 4.99 (0.12) 1.00 IS 0 0 0 04 139 4.97 (0.17) 0 0 0 01 142 4.98 (0.15) 0.92 OIQ 0 0 0 05 138 4.97 (0.19) 0 0 0 04 139 4.97 (0.17) 0.94 Figure 4. Axial CT KUB image acquired using AI-based automatic positioning technique (A). Axial CT KUB Image acquired using manual positioning technique (B). Discussion In this study, we evaluated the positioning accuracy, IQ, and RD of artificial intelligence (AI)-based automatic and manual positioning for CT Kidney Ureter Bladder (KUB) imaging. A closer look at off-center distances showed that the off-center positions were significantly lower in the AI-based AP group than in the MP group. The mean off-center distances for the AP and MP groups were 9.662±6.36 mm. and 15.117±9.55 mm. Similar findings were observed in a study by Yadong et al., in which the off-center distance was significantly higher in the MP group (4.05 ± 2.40 cm) than in the AP group (1.56 ± 0.83 cm) for CT thorax imaging. 14 The study was performed by Ronald et al. on pediatric patients with and without immobilization devices. They found that utilizing the 3D camera for positioning pediatric patients, without an additional immobilization device, resulted in more precise positioning compared to manual methods employed by radiographers, which is similar to the findings of adults. Notably, there was no difference in the positioning accuracy between the 3D camera and radiographers for patients placed with an immobilization device. 15 Saltybaeva et al. evaluated the accuracy of the 3D camera algorithm for AP and compared the results with those of MP for both chest and abdominal CT. For chest CT, the average difference in off-center was 7 ± 4 mm when using AP and 19 ± 9 mm when the table height was selected manually by technologists. For the abdomen, the average vertical off-centering was 4 ± 2 mm and 18 ± 11 mm for the automatic and MP respectively. 18 AEC techniques are perhaps the most important innovations in terms of dose reduction. When using an AEC, the tube current is adjusted automatically based on the size and attenuation of the anatomy. 22 – 24 Off-center anatomy can result in suboptimal exposure settings, which affect IQ and increase RD. 25 , 26 In our study, there were notable reductions in radiation dose metrics such as CTDIv (8.38%), DLP (12.32%), ED (12.42%), and SSDE (10.32%) in AI-based AP group compared to MP group. Yadong et al. observed 16% dose reduction for AP in CT thorax examinations compared to MP. 14 Dane et al. showed 23.8%, 22.8%, 17.2 %, and 20.5 % reductions in radiation dose for CT chest without contrast, abdominal pelvis enterography, chest with contrast, and abdomen pelvis contrast studies, respectively, for 3D camera-based positioning. 13 Similar findings were noted in a study by Aly et al., who showed a higher off-center distance in the MP group than in the AP group. Due to the higher off-center distance, the radiation dose parameters such as CTDIv (abdomen: 10.2±4.3, 9.8 ±5.5 mGy; thorax: 8.6±4.3, 8.5 ±3.9 mGy), DLP (abdomen: 485.8±221.9, 492.2 ± 293.6 mGy.cm; thorax: 310.80±221.5, 319.0 ± 188.9 mGy.cm) and SSDE (abdomen: 13.1 ± 4.1, 12.9 ± 4.3 mGy; thorax: 10.5 ± 4.1, 10.7 ± 3.7 mGy) was higher in MP group compared to AP group. 12 The AI-based AP group CT images showed lower IN quantitatively and qualitatively than the MP group because of the higher positioning accuracy with less off-center distance in the AP group, and the images were reconstructed using the DLIR algorithm. SNR and CNR were also higher in the AP group than in the MP group. The qualitative IQ parameters, such as IS (MP: 4.86 ± 0.34; AP: 4.97 ± 0.17) and OIQ (MP: 4.08 ± 0.26, AP: 4.97 ± 0.19), showed higher mean scores in the AP group than in the MP group. Similar findings were reported by Yadong et al., in which the IN was lower in the AP group than in the MP group. 14 , 27 Our study had some limitations. First, we did not measure and compare patients positioning time in automatic and manual positions. Second, we could not assess whether the off-center distance observed in the study during manual patient positioning was below or above the gantry isocenter. Third, we enrolled patients with a normal BMI in this study. However, we did not specifically address how variations in patient weight may affect the accuracy of patient positioning. Conclusion AI-based patient positioning is a touchless system that is operated by a single switch. The AI-based automatic positioning technique aligns the patient to the isocenter of the gantry with less off-center alignment and increases positioning accuracy. Hence, the study concludes that AI-based automatic positioning improves the overall image quality with noise reduction and reduced RD in patients undergoing CT KUB imaging. Further research in this area will improve the role of AI in healthcare optimization and patient care. Ethics and consent Ethical approval was obtained from the Institutional ethical committee (IEC 168/2023) of Kasturba Medical College and Hospital, Manipal, India on 7 th June 2023, and then the study was registered on Clinical Trial Registry- India (CTRI/2023/06/054173) on 20 th June 2023. Written Informed consent was obtained from all participants for publication and participation in the data collection for the study. Data availability Underlying data Figshare: AI based AP and MP CT KUB, https://doi.org/10.6084/m9.figshare.25641063.v3 . 28 This project contains the following underlying data: • Data_AP_MP (demographic details of patients, Qualitative and Quantitative analysis, radiation dose metrics, and off-center distance – Excel sheet) • AP KUB Images, MP KUB Images Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). References 1. Power SP, Moloney F, Twomey M, et al. : Computed tomography and patient risk: Facts, perceptions and uncertainties. World J. Radiol. 2016 28; 8 (12): 902–915. PubMed Abstract | Publisher Full Text | Free Full Text 2. Schmidt CW: CT scans: balancing health risks and medical benefits. Environ. Health Perspect. 2012; 120 (3): A118–A121. PubMed Abstract | Publisher Full Text 3. Cao CF, Ma KL, Shan H, et al. : CT scans and Cancer Risks: A Systematic Review and Dose-response Meta-analysis. 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Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 24 Jun 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India 2 Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India Souradip Kundu Roles: Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation Kaushik Nayak Roles: Data Curation, Formal Analysis, Investigation, Supervision, Writing – Original Draft Preparation Rajagopal Kadavigere Roles: Formal Analysis, Investigation, Methodology, Supervision, Validation, Writing – Review & Editing Saikiran Pendem Roles: Investigation, Methodology, Validation, Visualization, Writing – Review & Editing Priyanka . Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Supervision, Validation, Visualization, 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: 04 Jul 2024, 13:683 https://doi.org/10.12688/f1000research.150779.2 version 1 Published: 24 Jun 2024, 13:683 https://doi.org/10.12688/f1000research.150779.1 Copyright © 2024 Kundu S 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 Kundu S, Nayak K, Kadavigere R et al. Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.12688/f1000research.150779.2 ) 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 04 Jul 2024 Revised Views 0 Cite How to cite this report: Barde M. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.168776.r296054 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v2#referee-response-296054 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 09 Jul 2024 Mustapha Barde , Bayero University, Kano, Kano, Nigeria Approved VIEWS 0 https://doi.org/10.5256/f1000research.168776.r296054 The article presented a well sound approach in evaluating artificial intelligence (AI)-based method in comparison with manual patient positioning for CT KUB to ascertain the accuracy, radiation dose and image quality of the said methods. it was established that the ... Continue reading READ ALL The article presented a well sound approach in evaluating artificial intelligence (AI)-based method in comparison with manual patient positioning for CT KUB to ascertain the accuracy, radiation dose and image quality of the said methods. it was established that the AI based (automated position) is more robust Than the manual positioning, and it is recommended to be employed in clinical practice to optimize dose. The below minor corrections will be vital please. Minor correction The study duration (period within which it was conducted should be stated It will be good to state that, manual approach was employed in off center distance measurement. Also, the lines indicating the off-center distance measurement (figure 1) should be labelled eg ( p,q, r…etc) to aid comprehension Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Image analysis and processing, CT dosimetry 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 Barde M. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.168776.r296054 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v2#referee-response-296054 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: Shaikh T. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.168776.r299452 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v2#referee-response-299452 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 09 Jul 2024 Tasleem Shaikh , Urgent Care, MedStar Health, Alexandria, Virginia, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.168776.r299452 The revised ... Continue reading READ ALL The revised version is approved. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Medical Imaging Technology. 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 Shaikh T. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.168776.r299452 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v2#referee-response-299452 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: Priyanka C. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.168776.r299451 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v2#referee-response-299451 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 05 Jul 2024 Chandrasekhar Priyanka , Apollo University, Chittoor, Andhra Pradesh, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.168776.r299451 I approve ... Continue reading READ ALL I approve this revised version. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Computed Tomography,Magnetic Resonance Imaging, Radiation dose, Mammography and Fetal MRI 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 Priyanka C. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.168776.r299451 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v2#referee-response-299451 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 24 Jun 2024 Views 0 Cite How to cite this report: Shaikh T. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.165377.r296053 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v1#referee-response-296053 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 01 Jul 2024 Tasleem Shaikh , Urgent Care, MedStar Health, Alexandria, Virginia, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.165377.r296053 Major comments: The article highlights the advantages of AI-based automatic positioning using precise images in terms of positioning accuracy, radiation dose, and image quality compared to manual positioning for CT KUB examination. The introduction highlights the importance of ... Continue reading READ ALL Major comments: The article highlights the advantages of AI-based automatic positioning using precise images in terms of positioning accuracy, radiation dose, and image quality compared to manual positioning for CT KUB examination. The introduction highlights the importance of reducing radiation dose in CT examinations using AI-based automatic positioning, which improves positioning accuracy and image quality with adherence to the ALARA principle. The methodology is well explained. Inclusion criteria for patient selection, measurement of radiation dose parameters, image quality (quantitative and qualitative) parameters, off-center distance, and standardized protocols for both AI-based and manual positioning enhance the study's reproducibility. Appropriate statistical methods were used to analyze the data. The results section presents the findings clearly and organized, using appropriate tables and figures to illustrate the data. The significant improvements in positioning accuracy and reduction in radiation dose with AI-based positioning are well-documented. The 44% reduction in off-center distance suggests a substantial improvement in patient positioning accuracy, which is essential for optimal imaging. The statistically significant reductions in CTDIvol (8.38%), DLP (12.32%), SSDE (10.32%), and ED (12.42%) for the AP group compared to the MP group are noted. Higher qualitative IQ scores support the benefits of AI-based positioning in producing better diagnostic image quality. The almost perfect interobserver agreement suggests that these results are reliable and reproducible. The discussion effectively summarizes the results and implications of improved positioning accuracy and reduced radiation dose for clinical practice. AI's potential to enhance consistency and efficiency in CT KUB scans is well-articulated, and the limitations of the study are well-addressed. The conclusion highlights the potential of AI-based automatic positioning to improve clinical outcomes and reduce radiation exposure for CT KUB examinations. Minor comments: Ensure all the abbreviations are clearly defined when first used in the manuscript. Include a section on future research directions of the study. Suggest how the findings of this study could be applied or expanded in future research. Check for typographic errors in the units. The study is well structured, with clear comparisons and statistically significant results that highlight the potential of AI in CT imaging procedures, leading to better patient outcomes and improved workflow in radiology. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Medical Imaging Technology. 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 Shaikh T. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.165377.r296053 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v1#referee-response-296053 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: Priyanka C. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.165377.r296056 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v1#referee-response-296056 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 01 Jul 2024 Chandrasekhar Priyanka , Apollo University, Chittoor, Andhra Pradesh, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.165377.r296056 The article is interesting and innovative in exploring the advantages of AI-based positioning camera which detects the patients contour and allows for automatic positioning in CT KUB examination. Major comments The article highlights the AI-based positioning ability of ... Continue reading READ ALL The article is interesting and innovative in exploring the advantages of AI-based positioning camera which detects the patients contour and allows for automatic positioning in CT KUB examination. Major comments The article highlights the AI-based positioning ability of accurately positioning the patient with respect to the isocenter of gantry. It explains the benefits of reduced radiation dose by positioning the patient to isocenter in CT. The improvement in image quality with AI-based deep learning reconstruction algorithm (DLR) with automatic positioning such as precise image compared to iterative reconstruction (iDose 4 ) with manual positioning for CT KUB examinations was presented well in the article using qualitative and quantitative image quality analysis. Radiation is an important concern while performing CT scans. The present study also highlights the 12% reduction in radiation dose which is a substantial improvement, contributing to safer imaging practices. Minor comments: A small typographic error was noted in the units of off-center distance between abstract (mm) and results section (cm) which can be corrected. The study focused on CT KUB, requires exploration in other body parts for comprehensive understanding and realizing the full potential of AI-based positioning techniques in CT. Overall, I feel the article is excellent in the current era of utilizing the advantages of artificial intelligence-based technologies for optimizing the workflow, improving the patient care and comfort in CT. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Computed Tomography,Magnetic Resonance Imaging, Radiation dose, Mammography and Fetal MRI 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 Priyanka C. Reviewer Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.165377.r296056 ) The direct URL for this report is: https://f1000research.com/articles/13-683/v1#referee-response-296056 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 Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 24 Jun 2024 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) 04 Jul 24 read read read Version 1 24 Jun 24 read read Chandrasekhar Priyanka , Apollo University, Chittoor, Andhra Pradesh, India Tasleem Shaikh , MedStar Health, Alexandria, USA Mustapha Barde , Bayero University, Kano, Nigeria 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 © 2024 Barde M. 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. 09 Jul 2024 | for Version 2 Mustapha Barde , Bayero University, Kano, Kano, Nigeria 0 Views copyright © 2024 Barde M. 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 The article presented a well sound approach in evaluating artificial intelligence (AI)-based method in comparison with manual patient positioning for CT KUB to ascertain the accuracy, radiation dose and image quality of the said methods. it was established that the AI based (automated position) is more robust Than the manual positioning, and it is recommended to be employed in clinical practice to optimize dose. The below minor corrections will be vital please. Minor correction The study duration (period within which it was conducted should be stated It will be good to state that, manual approach was employed in off center distance measurement. Also, the lines indicating the off-center distance measurement (figure 1) should be labelled eg ( p,q, r…etc) to aid comprehension Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Image analysis and processing, CT dosimetry 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) Barde M. Peer Review Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.168776.r296054) 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/13-683/v2#referee-response-296054 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Shaikh T. 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. 09 Jul 2024 | for Version 2 Tasleem Shaikh , Urgent Care, MedStar Health, Alexandria, Virginia, USA 0 Views copyright © 2024 Shaikh T. 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 The revised version is approved. Competing Interests No competing interests were disclosed. Reviewer Expertise Medical Imaging Technology. 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) Shaikh T. Peer Review Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.168776.r299452) 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/13-683/v2#referee-response-299452 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Priyanka C. 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. 05 Jul 2024 | for Version 2 Chandrasekhar Priyanka , Apollo University, Chittoor, Andhra Pradesh, India 0 Views copyright © 2024 Priyanka C. 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 approve this revised version. Competing Interests No competing interests were disclosed. Reviewer Expertise Computed Tomography,Magnetic Resonance Imaging, Radiation dose, Mammography and Fetal MRI 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) Priyanka C. Peer Review Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.168776.r299451) 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/13-683/v2#referee-response-299451 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Shaikh T. 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. 01 Jul 2024 | for Version 1 Tasleem Shaikh , Urgent Care, MedStar Health, Alexandria, Virginia, USA 0 Views copyright © 2024 Shaikh T. 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 Major comments: The article highlights the advantages of AI-based automatic positioning using precise images in terms of positioning accuracy, radiation dose, and image quality compared to manual positioning for CT KUB examination. The introduction highlights the importance of reducing radiation dose in CT examinations using AI-based automatic positioning, which improves positioning accuracy and image quality with adherence to the ALARA principle. The methodology is well explained. Inclusion criteria for patient selection, measurement of radiation dose parameters, image quality (quantitative and qualitative) parameters, off-center distance, and standardized protocols for both AI-based and manual positioning enhance the study's reproducibility. Appropriate statistical methods were used to analyze the data. The results section presents the findings clearly and organized, using appropriate tables and figures to illustrate the data. The significant improvements in positioning accuracy and reduction in radiation dose with AI-based positioning are well-documented. The 44% reduction in off-center distance suggests a substantial improvement in patient positioning accuracy, which is essential for optimal imaging. The statistically significant reductions in CTDIvol (8.38%), DLP (12.32%), SSDE (10.32%), and ED (12.42%) for the AP group compared to the MP group are noted. Higher qualitative IQ scores support the benefits of AI-based positioning in producing better diagnostic image quality. The almost perfect interobserver agreement suggests that these results are reliable and reproducible. The discussion effectively summarizes the results and implications of improved positioning accuracy and reduced radiation dose for clinical practice. AI's potential to enhance consistency and efficiency in CT KUB scans is well-articulated, and the limitations of the study are well-addressed. The conclusion highlights the potential of AI-based automatic positioning to improve clinical outcomes and reduce radiation exposure for CT KUB examinations. Minor comments: Ensure all the abbreviations are clearly defined when first used in the manuscript. Include a section on future research directions of the study. Suggest how the findings of this study could be applied or expanded in future research. Check for typographic errors in the units. The study is well structured, with clear comparisons and statistically significant results that highlight the potential of AI in CT imaging procedures, leading to better patient outcomes and improved workflow in radiology. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Medical Imaging Technology. 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) Shaikh T. Peer Review Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.165377.r296053) 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/13-683/v1#referee-response-296053 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Priyanka C. 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. 01 Jul 2024 | for Version 1 Chandrasekhar Priyanka , Apollo University, Chittoor, Andhra Pradesh, India 0 Views copyright © 2024 Priyanka C. 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 The article is interesting and innovative in exploring the advantages of AI-based positioning camera which detects the patients contour and allows for automatic positioning in CT KUB examination. Major comments The article highlights the AI-based positioning ability of accurately positioning the patient with respect to the isocenter of gantry. It explains the benefits of reduced radiation dose by positioning the patient to isocenter in CT. The improvement in image quality with AI-based deep learning reconstruction algorithm (DLR) with automatic positioning such as precise image compared to iterative reconstruction (iDose 4 ) with manual positioning for CT KUB examinations was presented well in the article using qualitative and quantitative image quality analysis. Radiation is an important concern while performing CT scans. The present study also highlights the 12% reduction in radiation dose which is a substantial improvement, contributing to safer imaging practices. Minor comments: A small typographic error was noted in the units of off-center distance between abstract (mm) and results section (cm) which can be corrected. The study focused on CT KUB, requires exploration in other body parts for comprehensive understanding and realizing the full potential of AI-based positioning techniques in CT. Overall, I feel the article is excellent in the current era of utilizing the advantages of artificial intelligence-based technologies for optimizing the workflow, improving the patient care and comfort in CT. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Computed Tomography,Magnetic Resonance Imaging, Radiation dose, Mammography and Fetal MRI 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) Priyanka C. Peer Review Report For: Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB [version 2; peer review: 3 approved] . F1000Research 2024, 13 :683 ( https://doi.org/10.5256/f1000research.165377.r296056) 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/13-683/v1#referee-response-296056 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 Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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