Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units <800

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Chew, Victor KF. Wong, Abdulghafour Halawani, Sujin Lee, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3133615/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2023 Read the published version in Urolithiasis → Version 1 posted 7 You are reading this latest preprint version Abstract The correct diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical intervention and be offered alkalization therapy. We developed and validated a machine learning (ML)-based model to identify UA stones from non-UA stones. An international, multicenter study was performed on 202 patients who received percutaneous nephrolithotomy for kidney stones with HU < 800. Data from 156 (77.2%) patients were used for model development, while data from 46 (22.8%) patients from a multinational institution were used for external validation. A total of 21,074 kidney and stone contour-annotated computed tomography images were trained with the ResNet-18 Mask R-convolutional neural network algorithm. Finally, this model was concatenated with demographic and clinical data as a fully-connected layer for stone classification. Our model was 100% sensitive in detecting kidney stones in each patient, and the delineation of kidney and stone contours was precise within clinically acceptable ranges. The development model provided an accuracy of 99.9%, with 100.0% sensitivity and 98.9% specificity, in distinguishing UA from non-UA stones. On external validation, the model performed with an accuracy of 97.1%, with 89.4% sensitivity and 98.6% specificity. SHAP plots revealed stone density, diabetes mellitus, and urinary pH as the most important features for classification. Our ML-based model accurately identified and delineated kidney stones and classified UA stones from non-UA stones with the highest predictive accuracy reported to date. Our model can be reliably used to select candidates for an earlier-directed alkalization therapy. decision support techniques machine learning urolithiasis validation Figures Figure 1 Figure 2 1. Introduction The selection of a treatment modality for patients with kidney stones is based on stone size, location, density, instrument availability, and patient comorbidities. This choice affects postoperative outcomes and perioperative morbidity. Treatment modalities recommended by contemporary guidelines include ureteroscopic lithotripsy, percutaneous nephrolithotripsy (PCNL), extracorporeal shockwave lithotripsy, and alkalization therapy [ 1 – 3 ]. For selected stones, surgical treatment options may be equally effective regarding the outcome. Therefore, a specific treatment modality can be recommended based on the physician’s expertise and patient preference [ 1 ]. On the other hand, surgical intervention may compromise perioperative outcomes and increase morbidity in patients with significant comorbidities. In some patients, non-surgical modalities that are less invasive may be beneficial. Uric acid (UA) stones comprise 5–20% of all urolithiasis and are primarily explained by a low urinary pH, with a minority of patients exhibiting high urinary excretion of UA [ 4 , 5 ]. Medical dissolution therapy with urinary alkalization is the cornerstone of the medical management of UA stones, with a reported success rate of 80% and cost savings compared to surgical intervention [ 5 ]. The prediction of UA stones is not entirely accurate and is based on Hounsfield units (HU) < 450 on non-contrast-enhanced computed tomography (NCCT), radiolucency on plain radiography, and low urinary pH < 6 [ 6 ]. However, UA stones outside these ranges are often underdiagnosed due to a lack of reliable diagnostic systems predicting UA components and concerns about stones mistakenly thought to be UA [ 7 ]. As a result, alkalization therapy is reported to be underused despite its potential benefits [ 6 ]. Correct differential diagnosis of UA stones from other stone components has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical interventions. Several non-invasive techniques have been developed to classify UA from non-UA stones, including 24-hour urinalysis, nomograms using clinical and HU parameters, and dual-energy CT (DECT) [ 8 – 10 ]. However, these classification systems were limited by their model developments being based on a specific stone component, the necessity of access to particular CT scanner types, and, most importantly, the lack of external validation, which precludes their general applicability to clinical use. A growing body of evidence indicates that machine learning (ML) models may improve the accuracy of disease diagnosis and treatment outcomes compared to conventional discriminant analyses [ 11 ]. Given the multifactorial nature of the formation of specific stone components, we sought to develop an ML-based model that incorporates a comprehensive set of demographic, clinical, and CT data to better classify UA stones. The aims of our study were (1) to train and develop an ML-based urinary stone recognition algorithm that can automatically identify the stone location and delineate its contour relative to the kidney and (2) to utilize this algorithm to develop a prediction model that incorporates demographic, clinical, and NCCT data to classify UA stones from other stone components. 2. Materials and Methods 2.1. Study cohort An international, multicenter, cross-sectional study was conducted on 202 patients who underwent PCNL for kidney stones with HU < 800 between March 2005 and November 2018. The development cohort consisted of 156 (77.2%) patients from the Stone Centre at Vancouver General Hospital in Canada, while the external validation cohort comprised 46 (22.8%) patients from Gangnam Severance Hospital in Seoul, South Korea, a multinational institution with patients of a distinct ethnic background. Patients with incomplete data, including unknown stone composition, were excluded from the analysis. This study was approved by the institutional ethics committees of the University of British Columbia’s Clinical Research Ethics Board (H14-00475) and Gangnam Severance Hospital (2019-0838-001) after reviewing the protocols employed. All study procedures complied with the principles of the 1946 Declaration of Helsinki and its 2008 update. 2.2. Data acquisition The demographic and clinical data that potentially affect stone components and NCCT images were retrospectively collected from the patients’ medical records. Twenty-six preoperative demographic and clinical data included: patient age, body mass index, gender, American Society of Anesthesiologists score, stone history, number, bilaterality, multiplicity, HU, urinary pH and nitrite positivity, urine culture, serum levels of sodium, potassium, calcium, glomerular filtration rate, UA, phosphate, and the presence of comorbidities including cerebrovascular disease, chronic kidney disease, chronic obstructive pulmonary disease, diabetes mellitus, hypertension, gout, and neurological disease prior to PCNL (Table 1 ). Stone component data for both the development and external validation cohorts were based on analyses performed using Fournier-transform infrared spectrometry, carried out at Lifelabs, Burnaby, BC, Canada, and Green Cross Laboratories, Yongin, South Korea, respectively. Table 1 Demographic and clinical characteristics of the development and external validation cohorts. Development cohort External validation cohort P No. 156 (77.2%) 46 (22.8%) NS Age (year) 59.0 (47.0–68.3) 54.0 (37.0–64.5) 0.030 Body mass index (kg/m 2 ) 29.1 (24.3–33.3) 24.8 (21.8–27.6) < 0.001 Gender 0.467 Male 101 (64.7%) 26 (56.5%) Female 55 (35.3%) 20 (43.5%) Hounsfield units 499.4 (413.6–629.3) 545.5 (487.3–699.5) < 0.001 Stone, number 3.0 (2.0–4.5) 4.0 (3.0–6.5) 0.065 Stone component UA 41 (26.3%) 13 (28.3%) 0.143 Struvite 56 (35.9%) 14 (30.4%) 0.508 Calcium oxalate 54 (34.6%) 17 (37.0%) 0.236 Cystine 5 (3.2%) 2 (4.3%) 0.987 Urinary pH 5.5 (5.0–6.5) 6.5 (5.5–7.3) 0.003 Nitrite positivity 32 (20.5%) 9 (19.6%) 0.566 Urine culture positivity 31 (19.9%) 8 (17.4%) 0.648 Serum Sodium 140.0 (139.0–142.0) 140.0 (140.0–141.0) 0.587 Potassium 4.1 (3.8–4.4) 4.20 (4.10–4.38) 0.864 Calcium 2.33 (2.26–2.42) 2.31 (2.24–2.47) 0.873 Glomerular filtration rate 69.0 (50.0–94.0) 90.0 (74.0–121.5) < 0.001 UA 339.5 (273.5–431.5) 298.0 (206.5–350.5) 0.018 Phosphate 1.10 (0.92–1.29) 1.06 (0.96–1.28) 0.928 Bilaterality 42 (26.9%) 16 (34.8%) 0.040 Multiplicity 89 (57.1%) 37 (80.4%) 0.009 ASA score 0.062 0 38 (24.4%) 9 (19.6%) ≥ 1 118 (75.6%) 37 (80.4%) Stone history 40 (25.6%) 10 (21.7%) 0.098 Comorbidity Cerebrovascular disease 18 (11.5%) 5 (10.9%) 0.438 Chronic kidney disease 12 (7.7%) 3 (6.5%) 0.768 Chronic obstructive pulmonary disease 10 (6.4%) 2 (4.3%) 0.133 Diabetes mellitus 45 (28.8%) 10 (21.7%) 0.389 Hypertension 54 (34.6%) 20 (43.5%) 0.164 Gout 3 (1.9%) 0 (0.0%) 0.418 Neurological disease 20 (12.8%) 5 (10.9%) 0.129 Values are presented as number (%) or median (interquartile range). ASA = American Society of Anesthesiologists; ; COPD = chronic obstructive pulmonary disease; UA = uric acid Standard axial NCCT images that included kidney and stone information were acquired in DICOM format. After acquisition, the images were extracted using a Python software application and saved in PNG files. A total of 14,843 and 6,231 kidney and stone images were selected to form the datasets for developing the ML model. The anatomical contours of the kidneys and stones were semi-automatically annotated using the open-source Computer Vision Annotation Tool (Intel®, CA, USA). Overall, 21,074 kidney and stone annotated images were included in the datasets for model development. 2.3. Model development Figure 1 depicts the overall architecture of our model. Initially, the ResNet18 model framework was trained with the PyTorch feedforward deep learning library using predefined methods [ 12 ]. We used stochastic gradient descent as the optimizer with a learning rate set at 0.005 and a batch size of 32. The ResNet-18 Mask R-convolutional neural network model was chosen for this study due to its capability and robustness in general-purpose image segmentation under limited data [ 13 ]. The kidney and stone contour-annotated image data from the development cohort was used for model training, validation, and testing in an 8:1:1 ratio, with all images in the training set being different from those in the testing set. Finally, demographic and clinical data were concatenated with the ResNet18 model in a fully-connected layer to develop the final model for stone component classification. The model was then interpreted using the SHAP algorithm to enable visual interpretation of the quantitative association between the input variables and the model’s output [ 14 ]. 2.4. Evaluation metrics Prediction accuracies were analyzed according to binary (UA vs. non-UA stones) and multiclass (UA vs. calcium oxalate, struvite, and cystine stones) classifications. Prediction accuracies were measured by the precision of instance prediction, which counted the number of true positive, true negative, false positive, and false negative instances as compared with the operator’s semi-automatic annotation. Prediction accuracies were compared to those of the multivariate logistic regression analyses. 2.5. Statistical analysis Demographic and clinical characteristics between the development and external validation cohorts were compared using the two-sided Mann-Whitney U-test for the analysis of continuous variables and the chi-square test for the analysis of categorical variables. Logistic regression analysis for binary classification was performed using the same training set. Independent predictive indicators associated with UA stones in the multivariate analyses were entered into the logistic regression model. All tests were two-tailed, with statistical significance set at a p < 0.05. Statistical analysis was performed using IBM SPSS Statistics software ver. 21.0 (IBM Corporation, Armonk, NY) and R Statistical Package ver. 3.1.3. (Institute for Statistics and Mathematics, Vienna, Austria). 3. Results 3.1. Demographic and clinical features The demographic and clinical characteristics of patients in the development and external validation cohorts are presented in Table 1 . According to stone components, UA, calcium oxalate, struvite, and cystine stones comprised 26.3%, 35.9%, 34.6%, and 3.2% in the development cohort, while 28.3%, 30.4%, 37.0%, and 4.3% in the external validation cohort, respectively. Overall, there were no significant differences between the development and external validation cohorts regarding demographic, clinical, and stone features. 3.2. Kidney and stone identification and contour delineation Segmentation identified and delineated two anatomical elements, kidney and stone contours. Our model was 100% sensitive in detecting kidney stones in each patient. The delineation of kidney and stone contours was precise within clinically acceptable ranges, as shown in a selected sample patient (Fig. 2 ). 3.3. Predictive performance The predictive accuracies of our model varied according to the stone component and classification system. Overall, the performances of the ML-based model outperformed those of the logistic regression model (Table 2 ). Table 2 Predictive accuracies of the development and external validation cohorts. Classification Cohort Stone type Accuracy (%) Sensitivity (%) Specificity (%) LR Binary Development UA vs. non-UA 82.4 72.4 69.6 External validation UA vs. non-UA 86.9 82.6 73.3 ML Binary Development UA vs. non-UA 99.9 100.0 99.9 External validation UA vs. non-UA 97.1 89.4 98.6 Multiclass Development UA 98.2 95.7 89.6 Calcium oxalate 88.4 70.7 98.4 Struvite 98.7 97.7 99.2 Cystine 95.5 77.1 96 External validation UA 91.3 77.1 89.6 Calcium oxalate 74.3 95.8 55.8 Struvite 89.4 72.5 97.2 Cystine 93.5 0.0 93.5 UA = uric acid 3.3.1. Binary classification The development model discriminated UA and non-UA stones with an accuracy of 99.9%, with 100.0% sensitivity and 99.9% specificity (Table 2 ). We identified features most predictive for binary stone classification by quantifying the predictor importance of each variable. Stone density, diabetes mellitus, and urinary pH showed to be the top three contributing features for classifying UA stones. On external validation, the model performed with a predictive accuracy of 97.1%, with 89.4% sensitivity and 98.6% specificity. The ML-based model exhibited higher performance than the multivariate logistic regression model in both development and external validation cohorts. 3.3.2. Multiclass classification The development model discriminated UA, calcium oxalate, struvite, and cystine stones with predictive performances of 98.2%, 88.4%, 98.7%, and 95.5%, respectively (Table 2 ). The features most predictive for multiclass stone classification were stone density, diabetes mellitus, and urinary pH. On external validation, the model’s prediction accuracies for UA, calcium oxalate, struvite, and cystine stones were 91.3%, 74.3%, 89.4%, and 93.5%, respectively. The ML-based model’s performance for multiclass classification was relatively lower than that of the binary classification; however, remained within clinically acceptable ranges. 4. Discussion The inspiration for this study arises from the unmet clinical need to accurately predict UA stones prior to selecting the optimal treatment modality. Over the last decade, technological advancements in ureteroscopes and laser lithotriptors have paved the path to an upsurge in surgical intervention regardless of stone composition [ 15 , 16 ]. Although alkalization therapy for UA stones is ideal for patients with high morbidity or recurrent UA stone formers, it is commonly underused due to the lack of reliable factors predicting its outcome, concerns about the existence of heterogeneous stone composition, and patient intolerance [ 7 – 15 ]. Most of all, the lack of standardized protocols for predicting UA components adds complexity to making treatment decisions in real-life situations [ 15 ]. The present study is the first to develop and validate an effective predictive model incorporating NCCT images into traditional demographic and clinical data to classify UA from non-UA stones in patients with stones in the ‘grey zone’ HUs. External validation showed that our objective, expeditious, and non-invasive model could identify UA stones with an accuracy of 97.1%, the highest predictive performance reported to date. Our model has several implications for improving the current standard of care through its implementation in clinical practice. First, the input variables, including demographic, clinical, and NCCT data, are those readily available in real-world practice, which supports the general applicability of our model. Previously reported stone component classification models utilizing imaging data generally require time-consuming manual analysis of HU parameters or additional examination using specific CT scanner types, such as DECT, which may not be available across all practice settings [ 8 , 17 – 19 ]. In contrast, our automated model has the potential to be integrated into any electronic medical records system that utilizes coding algorithms to be utilized as a decision support system. Such a system may reduce the time required for classification and avoid additional radiation exposure and costs. Second, we selected patients with stones of relatively low HUs for the model development since these stones pose a diagnostic dilemma in clinical decision-making for alkalization therapy [ 5 ]. We selected stones with HUs < 800 in order to include struvite and cystine stones, in addition to UA stones, that are characterized as having a completely distinct management approach. The multiclass classification model provided a relatively lower performance than the binary classification. However, the overall performance was excellent and surpassed that of the conventional multivariate logistic regression model, providing a reliable diagnostic standard for treatment decision-making. Lastly, the architecture of our model and its working principle allow future refinements. Our model can additionally integrate intraoperative laser lithotripsy data and has the potential to provide patient-specific optimal laser settings for maximal fragmentation efficiency according to each stone feature. Several strengths of our study are worth mentioning. First, external validation of prediction models is essential before their use in clinical practice. Since validation samples should be obtained from different but plausibly relevant cohorts, the performance of our model was validated with an external cohort comprised of patients from an international institution with distinct ethnic backgrounds. Discrimination performance is usually observed to be inferior in the external validation cohort compared to the development cohort [ 20 ]. Nevertheless, the performance of our external validation cohort was non-inferior compared to that of the development cohort, indicating the validity and feasibility of our model. Second, we incorporated a comprehensive set of demographic, clinical, and NCCT imaging data that are potentially associated with stone components for the model development. Moreover, the dataset was considered of high quality, with all input variables of the development and external validation cohorts being manually reviewed and incorporated without any missing data, which may have contributed to its high predictive performance. This study is not without limitations. First, mixed component stones were excluded from the development and external validation cohorts. Although the distinction between pure UA stones and mixed component stones is crucial in the decision-making of alkalization therapy, only stones with pure components were included. Since the extent of the UA component beyond which the stone has to be defined as mixed is unclear, subsequent studies incorporating quantitative analysis of mixed stones, will need to be performed to screen optimal patients who would be amenable to medical therapy. Second, a population-based database with a larger number of subjects may provide better generalizability. Albeit, we utilized institutional data, which provided a comprehensive and high-quality dataset, to maximize predictive performance. Lastly, performances declined for the multiclass classification, indicating uncertainty of clinical usefulness, especially in classifying cystine stones. The likely explanation is the limited number of cystine stones in both the development and external validation cohorts. Notwithstanding these limitations, the advantages of our model over previously reported tools classifying predicting stone components indicate its feasibility and general applicability to be implemented into real-world clinical practice. 5. Conclusions We developed and externally validated an ML-based model to identify and delineate kidney stones and classify UA stones from other stone components. With the highest predictive performance reported to date, our model can be reliably used to select candidates for an earlier-directed alkalization therapy in patients with kidney stones within the ‘grey zone’ HUs. Further modification in the ML algorithm incorporating cases with mixed component stones would be warranted for more sophisticated predictions. Declarations 6. Acknowledgments This study was supported by a faculty research grant of Yonsei University College of Medicine (6-2022-0173). 7. Conflict of interest No competing financial interests exist for all authors, including Ben H. Chew, Victor KF. Wong, Abdulghafour Halawani, Sujin Lee, Sangyeop Baek, Hoyong Kang, and Kyo Chul Koo. 8. Author contributions Conceptualization: Kyo Chul Koo and Ben H. Chew; Methodology: Victor KF. Wong and Abdulghafour Halawani; Formal analysis and investigation: Sujin Lee, Sangyeop Baek, and Hoyong Kang; Writing - original draft preparation: Kyo Chul Koo; Writing - review and editing: Victor KF. Wong, Abdulghafour Halawani, Sujin Lee, Sangyeop Baek, and Hoyong Kang; Funding acquisition: Kyo Chul Koo; Resources: Ben H. Chew; Supervision: Kyo Chul Koo and Ben H. Chew 9. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. 10. Data availability The data sets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Geraghty RM, Davis NF, Tzelves L, et al. (2022) Best Practice in Interventional Management of Urolithiasis: An Update from the European Association of Urology Guidelines Panel for Urolithiasis 2022. Eur Urol Focus; doi: 10.1016/j.euf.2022.06.014. Quhal F, Seitz C (2021) Guideline of the guidelines: urolithiasis. Curr Opin Urol 31:125-129; doi: 10.1097/MOU.0000000000000855. Turk C, Petrik A, Sarica K, et al. (2016) EAU Guidelines on Interventional Treatment for Urolithiasis. Eur Urol 69:475-482; doi: 10.1016/j.eururo.2015.07.041. 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Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2023 Read the published version in Urolithiasis → Version 1 posted Editorial decision: Major revision 30 Aug, 2023 Reviews received at journal 26 Jul, 2023 Reviewers agreed at journal 17 Jul, 2023 Reviewers invited by journal 14 Jul, 2023 Submission checks completed at journal 03 Jul, 2023 Editor assigned by journal 03 Jul, 2023 First submitted to journal 02 Jul, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3133615","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":215162444,"identity":"44f90b51-2399-45fd-a22d-587bf889591f","order_by":0,"name":"Ben H. Chew","email":"","orcid":"","institution":"University of British Columbia, Stone Centre at Vancouver General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"H.","lastName":"Chew","suffix":""},{"id":215162446,"identity":"fdb7770b-a04c-4b64-bdad-46c26eefed90","order_by":1,"name":"Victor KF. Wong","email":"","orcid":"","institution":"University of British Columbia, Stone Centre at Vancouver General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"KF.","lastName":"Wong","suffix":""},{"id":215162448,"identity":"0df0fbc5-7035-4cf2-a935-d318153e2eae","order_by":2,"name":"Abdulghafour Halawani","email":"","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Abdulghafour","middleName":"","lastName":"Halawani","suffix":""},{"id":215162449,"identity":"7563918c-4dec-41c0-9870-218622909781","order_by":3,"name":"Sujin Lee","email":"","orcid":"","institution":"Infinyx, AI research team","correspondingAuthor":false,"prefix":"","firstName":"Sujin","middleName":"","lastName":"Lee","suffix":""},{"id":215162452,"identity":"dee4baa6-9e27-4082-86cd-6b7b73fbbc02","order_by":4,"name":"Sangyeop Baek","email":"","orcid":"","institution":"Infinyx, AI research team","correspondingAuthor":false,"prefix":"","firstName":"Sangyeop","middleName":"","lastName":"Baek","suffix":""},{"id":215162453,"identity":"b9a4b2d5-f4ab-4c48-b6e0-9c454fb11635","order_by":5,"name":"Hoyong Kang","email":"","orcid":"","institution":"Infinyx, AI research team","correspondingAuthor":false,"prefix":"","firstName":"Hoyong","middleName":"","lastName":"Kang","suffix":""},{"id":215162454,"identity":"869dfdac-38f2-46eb-9cf4-598f899e5657","order_by":6,"name":"Kyo Chul Koo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYFACNiCuYGAwIFHLGZK1MLaRokXe/Vjig4/zDidu5z/A+OEHMVoMz6QdNpy57XDizhkJzJI9RGmZwd4mzQvUsuEGA4M0UQ6DaJkD1HL+APNvorTIS7Adk+ZtAGo5kMBGnC0GPGnJhjOOpRtvuJHYZkmUX+Tbjxk++FBjLbvh/OHDN4gKMYMDYKoZiBkbiHIXgzxEXR1xqkfBKBgFo2BkAgB8LzTfPz+OLQAAAABJRU5ErkJggg==","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Kyo","middleName":"Chul","lastName":"Koo","suffix":""}],"badges":[],"createdAt":"2023-07-02 21:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3133615/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3133615/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00240-023-01490-y","type":"published","date":"2023-09-30T15:01:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39643457,"identity":"97b9a8f2-52d3-45c8-9e01-870747fa713c","added_by":"auto","created_at":"2023-07-06 14:40:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89283,"visible":true,"origin":"","legend":"\u003cp\u003eOverall architecture of our model: First, the ResNet18 Mask R-convolutional neural network model framework was trained using predefined methods with the PyTorch feedforward deep learning library. Next, demographic and clinical data were concatenated with the ResNet18 model in a fully-connected layer to develop the final model for stone component classification.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3133615/v1/464fefa7b1cd974a386ad726.png"},{"id":39642421,"identity":"ebe97c92-24e7-45ee-bc55-13232ae9a1d6","added_by":"auto","created_at":"2023-07-06 14:32:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":205732,"visible":true,"origin":"","legend":"\u003cp\u003eA patient-specific human-annotated and model-interpreted kidney and stone contour segment of a chosen sample patient (subject no. 78).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3133615/v1/c22f08e60234122248a6f0eb.png"},{"id":43974552,"identity":"d9992263-d1b7-4ac6-b017-92f10790d9c3","added_by":"auto","created_at":"2023-10-02 15:08:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":660542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3133615/v1/93bd3ee1-ba43-4866-abe8-51b2f20e4b63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units \u003c800","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe selection of a treatment modality for patients with kidney stones is based on stone size, location, density, instrument availability, and patient comorbidities. This choice affects postoperative outcomes and perioperative morbidity. Treatment modalities recommended by contemporary guidelines include ureteroscopic lithotripsy, percutaneous nephrolithotripsy (PCNL), extracorporeal shockwave lithotripsy, and alkalization therapy [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For selected stones, surgical treatment options may be equally effective regarding the outcome. Therefore, a specific treatment modality can be recommended based on the physician\u0026rsquo;s expertise and patient preference [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. On the other hand, surgical intervention may compromise perioperative outcomes and increase morbidity in patients with significant comorbidities. In some patients, non-surgical modalities that are less invasive may be beneficial.\u003c/p\u003e \u003cp\u003eUric acid (UA) stones comprise 5\u0026ndash;20% of all urolithiasis and are primarily explained by a low urinary pH, with a minority of patients exhibiting high urinary excretion of UA [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Medical dissolution therapy with urinary alkalization is the cornerstone of the medical management of UA stones, with a reported success rate of 80% and cost savings compared to surgical intervention [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The prediction of UA stones is not entirely accurate and is based on Hounsfield units (HU)\u0026thinsp;\u0026lt;\u0026thinsp;450 on non-contrast-enhanced computed tomography (NCCT), radiolucency on plain radiography, and low urinary pH\u0026thinsp;\u0026lt;\u0026thinsp;6 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, UA stones outside these ranges are often underdiagnosed due to a lack of reliable diagnostic systems predicting UA components and concerns about stones mistakenly thought to be UA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As a result, alkalization therapy is reported to be underused despite its potential benefits [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Correct differential diagnosis of UA stones from other stone components has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical interventions.\u003c/p\u003e \u003cp\u003eSeveral non-invasive techniques have been developed to classify UA from non-UA stones, including 24-hour urinalysis, nomograms using clinical and HU parameters, and dual-energy CT (DECT) [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, these classification systems were limited by their model developments being based on a specific stone component, the necessity of access to particular CT scanner types, and, most importantly, the lack of external validation, which precludes their general applicability to clinical use. A growing body of evidence indicates that machine learning (ML) models may improve the accuracy of disease diagnosis and treatment outcomes compared to conventional discriminant analyses [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Given the multifactorial nature of the formation of specific stone components, we sought to develop an ML-based model that incorporates a comprehensive set of demographic, clinical, and CT data to better classify UA stones.\u003c/p\u003e \u003cp\u003eThe aims of our study were (1) to train and develop an ML-based urinary stone recognition algorithm that can automatically identify the stone location and delineate its contour relative to the kidney and (2) to utilize this algorithm to develop a prediction model that incorporates demographic, clinical, and NCCT data to classify UA stones from other stone components.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study cohort\u003c/h2\u003e \u003cp\u003eAn international, multicenter, cross-sectional study was conducted on 202 patients who underwent PCNL for kidney stones with HU\u0026thinsp;\u0026lt;\u0026thinsp;800 between March 2005 and November 2018. The development cohort consisted of 156 (77.2%) patients from the Stone Centre at Vancouver General Hospital in Canada, while the external validation cohort comprised 46 (22.8%) patients from Gangnam Severance Hospital in Seoul, South Korea, a multinational institution with patients of a distinct ethnic background. Patients with incomplete data, including unknown stone composition, were excluded from the analysis.\u003c/p\u003e \u003cp\u003e This study was approved by the institutional ethics committees of the University of British Columbia\u0026rsquo;s Clinical Research Ethics Board (H14-00475) and Gangnam Severance Hospital (2019-0838-001) after reviewing the protocols employed. All study procedures complied with the principles of the 1946 Declaration of Helsinki and its 2008 update.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data acquisition\u003c/h2\u003e \u003cp\u003eThe demographic and clinical data that potentially affect stone components and NCCT images were retrospectively collected from the patients\u0026rsquo; medical records. Twenty-six preoperative demographic and clinical data included: patient age, body mass index, gender, American Society of Anesthesiologists score, stone history, number, bilaterality, multiplicity, HU, urinary pH and nitrite positivity, urine culture, serum levels of sodium, potassium, calcium, glomerular filtration rate, UA, phosphate, and the presence of comorbidities including cerebrovascular disease, chronic kidney disease, chronic obstructive pulmonary disease, diabetes mellitus, hypertension, gout, and neurological disease prior to PCNL (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Stone component data for both the development and external validation cohorts were based on analyses performed using Fournier-transform infrared spectrometry, carried out at Lifelabs, Burnaby, BC, Canada, and Green Cross Laboratories, Yongin, South Korea, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of the development and external validation cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopment cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExternal validation cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156 (77.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.0 (47.0\u0026ndash;68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.0 (37.0\u0026ndash;64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.030\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.1 (24.3\u0026ndash;33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.8 (21.8\u0026ndash;27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.467\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHounsfield units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e499.4 (413.6\u0026ndash;629.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e545.5 (487.3\u0026ndash;699.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStone, number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.0\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.065\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStone component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.143\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStruvite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.508\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium oxalate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.236\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.987\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.5 (5.0\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5 (5.5\u0026ndash;7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrite positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.566\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine culture positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.648\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140.0 (139.0\u0026ndash;142.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140.0 (140.0\u0026ndash;141.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.587\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.1 (3.8\u0026ndash;4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.20 (4.10\u0026ndash;4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.864\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.33 (2.26\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.31 (2.24\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.873\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular filtration rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.0 (50.0\u0026ndash;94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.0 (74.0\u0026ndash;121.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e339.5 (273.5\u0026ndash;431.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e298.0 (206.5\u0026ndash;350.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.018\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.92\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.96\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.928\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilaterality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.040\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiplicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (80.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.009\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.062\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118 (75.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (80.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStone history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.098\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.438\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.768\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.133\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.389\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.164\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.418\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.129\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are presented as number (%) or median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eASA\u0026thinsp;=\u0026thinsp;American Society of Anesthesiologists; ; COPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease; UA\u0026thinsp;=\u0026thinsp;uric acid\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStandard axial NCCT images that included kidney and stone information were acquired in DICOM format. After acquisition, the images were extracted using a Python software application and saved in PNG files. A total of 14,843 and 6,231 kidney and stone images were selected to form the datasets for developing the ML model. The anatomical contours of the kidneys and stones were semi-automatically annotated using the open-source Computer Vision Annotation Tool (Intel\u0026reg;, CA, USA). Overall, 21,074 kidney and stone annotated images were included in the datasets for model development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Model development\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the overall architecture of our model. Initially, the ResNet18 model framework was trained with the PyTorch feedforward deep learning library using predefined methods [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We used stochastic gradient descent as the optimizer with a learning rate set at 0.005 and a batch size of 32. The ResNet-18 Mask R-convolutional neural network model was chosen for this study due to its capability and robustness in general-purpose image segmentation under limited data [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The kidney and stone contour-annotated image data from the development cohort was used for model training, validation, and testing in an 8:1:1 ratio, with all images in the training set being different from those in the testing set. Finally, demographic and clinical data were concatenated with the ResNet18 model in a fully-connected layer to develop the final model for stone component classification. The model was then interpreted using the SHAP algorithm to enable visual interpretation of the quantitative association between the input variables and the model\u0026rsquo;s output [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Evaluation metrics\u003c/h2\u003e \u003cp\u003ePrediction accuracies were analyzed according to binary (UA vs. non-UA stones) and multiclass (UA vs. calcium oxalate, struvite, and cystine stones) classifications. Prediction accuracies were measured by the precision of instance prediction, which counted the number of true positive, true negative, false positive, and false negative instances as compared with the operator\u0026rsquo;s semi-automatic annotation. Prediction accuracies were compared to those of the multivariate logistic regression analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eDemographic and clinical characteristics between the development and external validation cohorts were compared using the two-sided Mann-Whitney U-test for the analysis of continuous variables and the chi-square test for the analysis of categorical variables. Logistic regression analysis for binary classification was performed using the same training set. Independent predictive indicators associated with UA stones in the multivariate analyses were entered into the logistic regression model. All tests were two-tailed, with statistical significance set at a \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analysis was performed using IBM SPSS Statistics software ver. 21.0 (IBM Corporation, Armonk, NY) and R Statistical Package ver. 3.1.3. (Institute for Statistics and Mathematics, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Demographic and clinical features\u003c/h2\u003e \u003cp\u003eThe demographic and clinical characteristics of patients in the development and external validation cohorts are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. According to stone components, UA, calcium oxalate, struvite, and cystine stones comprised 26.3%, 35.9%, 34.6%, and 3.2% in the development cohort, while 28.3%, 30.4%, 37.0%, and 4.3% in the external validation cohort, respectively. Overall, there were no significant differences between the development and external validation cohorts regarding demographic, clinical, and stone features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Kidney and stone identification and contour delineation\u003c/h2\u003e \u003cp\u003eSegmentation identified and delineated two anatomical elements, kidney and stone contours. Our model was 100% sensitive in detecting kidney stones in each patient. The delineation of kidney and stone contours was precise within clinically acceptable ranges, as shown in a selected sample patient (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Predictive performance\u003c/h2\u003e \u003cp\u003eThe predictive accuracies of our model varied according to the stone component and classification system. Overall, the performances of the ML-based model outperformed those of the logistic regression model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive accuracies of the development and external validation cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStone type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA vs. non-UA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExternal validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA vs. non-UA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA vs. non-UA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExternal validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA vs. non-UA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulticlass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalcium oxalate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStruvite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCystine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExternal validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalcium oxalate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStruvite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCystine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eUA\u0026thinsp;=\u0026thinsp;uric acid\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Binary classification\u003c/h2\u003e \u003cp\u003eThe development model discriminated UA and non-UA stones with an accuracy of 99.9%, with 100.0% sensitivity and 99.9% specificity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We identified features most predictive for binary stone classification by quantifying the predictor importance of each variable. Stone density, diabetes mellitus, and urinary pH showed to be the top three contributing features for classifying UA stones. On external validation, the model performed with a predictive accuracy of 97.1%, with 89.4% sensitivity and 98.6% specificity. The ML-based model exhibited higher performance than the multivariate logistic regression model in both development and external validation cohorts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Multiclass classification\u003c/h2\u003e \u003cp\u003eThe development model discriminated UA, calcium oxalate, struvite, and cystine stones with predictive performances of 98.2%, 88.4%, 98.7%, and 95.5%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The features most predictive for multiclass stone classification were stone density, diabetes mellitus, and urinary pH. On external validation, the model\u0026rsquo;s prediction accuracies for UA, calcium oxalate, struvite, and cystine stones were 91.3%, 74.3%, 89.4%, and 93.5%, respectively. The ML-based model\u0026rsquo;s performance for multiclass classification was relatively lower than that of the binary classification; however, remained within clinically acceptable ranges.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe inspiration for this study arises from the unmet clinical need to accurately predict UA stones prior to selecting the optimal treatment modality. Over the last decade, technological advancements in ureteroscopes and laser lithotriptors have paved the path to an upsurge in surgical intervention regardless of stone composition [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although alkalization therapy for UA stones is ideal for patients with high morbidity or recurrent UA stone formers, it is commonly underused due to the lack of reliable factors predicting its outcome, concerns about the existence of heterogeneous stone composition, and patient intolerance [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Most of all, the lack of standardized protocols for predicting UA components adds complexity to making treatment decisions in real-life situations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The present study is the first to develop and validate an effective predictive model incorporating NCCT images into traditional demographic and clinical data to classify UA from non-UA stones in patients with stones in the \u0026lsquo;grey zone\u0026rsquo; HUs. External validation showed that our objective, expeditious, and non-invasive model could identify UA stones with an accuracy of 97.1%, the highest predictive performance reported to date.\u003c/p\u003e \u003cp\u003eOur model has several implications for improving the current standard of care through its implementation in clinical practice. First, the input variables, including demographic, clinical, and NCCT data, are those readily available in real-world practice, which supports the general applicability of our model. Previously reported stone component classification models utilizing imaging data generally require time-consuming manual analysis of HU parameters or additional examination using specific CT scanner types, such as DECT, which may not be available across all practice settings [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, our automated model has the potential to be integrated into any electronic medical records system that utilizes coding algorithms to be utilized as a decision support system. Such a system may reduce the time required for classification and avoid additional radiation exposure and costs.\u003c/p\u003e \u003cp\u003eSecond, we selected patients with stones of relatively low HUs for the model development since these stones pose a diagnostic dilemma in clinical decision-making for alkalization therapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. We selected stones with HUs\u0026thinsp;\u0026lt;\u0026thinsp;800 in order to include struvite and cystine stones, in addition to UA stones, that are characterized as having a completely distinct management approach. The multiclass classification model provided a relatively lower performance than the binary classification. However, the overall performance was excellent and surpassed that of the conventional multivariate logistic regression model, providing a reliable diagnostic standard for treatment decision-making. Lastly, the architecture of our model and its working principle allow future refinements. Our model can additionally integrate intraoperative laser lithotripsy data and has the potential to provide patient-specific optimal laser settings for maximal fragmentation efficiency according to each stone feature.\u003c/p\u003e \u003cp\u003eSeveral strengths of our study are worth mentioning. First, external validation of prediction models is essential before their use in clinical practice. Since validation samples should be obtained from different but plausibly relevant cohorts, the performance of our model was validated with an external cohort comprised of patients from an international institution with distinct ethnic backgrounds. Discrimination performance is usually observed to be inferior in the external validation cohort compared to the development cohort [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nevertheless, the performance of our external validation cohort was non-inferior compared to that of the development cohort, indicating the validity and feasibility of our model. Second, we incorporated a comprehensive set of demographic, clinical, and NCCT imaging data that are potentially associated with stone components for the model development. Moreover, the dataset was considered of high quality, with all input variables of the development and external validation cohorts being manually reviewed and incorporated without any missing data, which may have contributed to its high predictive performance.\u003c/p\u003e \u003cp\u003eThis study is not without limitations. First, mixed component stones were excluded from the development and external validation cohorts. Although the distinction between pure UA stones and mixed component stones is crucial in the decision-making of alkalization therapy, only stones with pure components were included. Since the extent of the UA component beyond which the stone has to be defined as mixed is unclear, subsequent studies incorporating quantitative analysis of mixed stones, will need to be performed to screen optimal patients who would be amenable to medical therapy. Second, a population-based database with a larger number of subjects may provide better generalizability. Albeit, we utilized institutional data, which provided a comprehensive and high-quality dataset, to maximize predictive performance. Lastly, performances declined for the multiclass classification, indicating uncertainty of clinical usefulness, especially in classifying cystine stones. The likely explanation is the limited number of cystine stones in both the development and external validation cohorts. Notwithstanding these limitations, the advantages of our model over previously reported tools classifying predicting stone components indicate its feasibility and general applicability to be implemented into real-world clinical practice.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWe developed and externally validated an ML-based model to identify and delineate kidney stones and classify UA stones from other stone components. With the highest predictive performance reported to date, our model can be reliably used to select candidates for an earlier-directed alkalization therapy in patients with kidney stones within the \u0026lsquo;grey zone\u0026rsquo; HUs. Further modification in the ML algorithm incorporating cases with mixed component stones would be warranted for more sophisticated predictions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a faculty research grant of Yonsei University College of Medicine (6-2022-0173).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e7. Conflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing financial interests exist for all authors, including Ben H. Chew, Victor KF. Wong, Abdulghafour Halawani, Sujin Lee, Sangyeop Baek, Hoyong Kang, and Kyo Chul Koo.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e8. Author contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Kyo Chul Koo and Ben H. Chew; Methodology: Victor KF. Wong and Abdulghafour Halawani; Formal analysis and investigation: Sujin Lee, Sangyeop Baek, and Hoyong Kang; Writing - original draft preparation: Kyo Chul Koo; Writing - review and editing: Victor KF. Wong, Abdulghafour Halawani, Sujin Lee, Sangyeop Baek, and Hoyong Kang; Funding acquisition: Kyo Chul Koo; Resources: Ben H. Chew; Supervision: Kyo Chul Koo and Ben H. Chew\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e9. Ethical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e10. Data availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGeraghty RM, Davis NF, Tzelves L, et al. (2022) Best Practice in Interventional Management of Urolithiasis: An Update from the European Association of Urology Guidelines Panel for Urolithiasis 2022. Eur Urol Focus; doi: 10.1016/j.euf.2022.06.014.\u003c/li\u003e\n\u003cli\u003eQuhal F, Seitz C (2021) Guideline of the guidelines: urolithiasis. Curr Opin Urol 31:125-129; doi: 10.1097/MOU.0000000000000855.\u003c/li\u003e\n\u003cli\u003eTurk C, Petrik A, Sarica K, et al. (2016) EAU Guidelines on Interventional Treatment for Urolithiasis. Eur Urol 69:475-482; doi: 10.1016/j.eururo.2015.07.041.\u003c/li\u003e\n\u003cli\u003eTrinchieri A, Montanari E (2018) Biochemical and dietary factors of uric acid stone formation. Urolithiasis 46:167-172; doi: 10.1007/s00240-017-0965-2.\u003c/li\u003e\n\u003cli\u003eShekarriz B, Stoller ML (2002) Uric acid nephrolithiasis: current concepts and controversies. J Urol 168(4 Pt 1):1307-1314; doi: 10.1097/00005392-200210010-00003.\u003c/li\u003e\n\u003cli\u003eTsaturyan A, Bokova E, Bosshard P, et al. (2020) Oral chemolysis is an effective, non-invasive therapy for urinary stones suspected of uric acid content. Urolithiasis 48:501-507; doi: 10.1007/s00240-020-01204-8.\u003c/li\u003e\n\u003cli\u003eMoore J, Nevo A, Salih S, et al. (2022) Outcomes and rates of dissolution therapy for uric acid stones. J Nephrol 35:665-669; doi: 10.1007/s40620-021-01094-y.\u003c/li\u003e\n\u003cli\u003eMcGrath TA, Frank RA, Schieda N, et al. (2020) Diagnostic accuracy of dual-energy computed tomography (DECT) to differentiate uric acid from non-uric acid calculi: systematic review and meta-analysis. Eur Radiol 30:2791-2801; doi: 10.1007/s00330-019-06559-0.\u003c/li\u003e\n\u003cli\u003eMoreira DM, Friedlander JI, Hartman C, et al. (2013) Using 24-hour urinalysis to predict stone type. J Urol 190:2106-2011; doi: 10.1016/j.juro.2013.05.115.\u003c/li\u003e\n\u003cli\u003eWiessmeyer JR, Ozimek T, Struck JP, et al. (2022) Comprehensive Nomogram for Prediction of the Uric Acid Composition of Ureteral Stones as a Part of Tailored Stone Therapy. Eur Urol Focus 8:291-296; doi: 10.1016/j.euf.2021.02.001.\u003c/li\u003e\n\u003cli\u003eLim B, Lee KS, Lee YH, et al. (2021) External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival. Cancer Res Treat 53:558-566; doi: 10.4143/crt.2020.637.\u003c/li\u003e\n\u003cli\u003eHe K, Zhang X, Ren S, et al. (2015) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770-778.\u003c/li\u003e\n\u003cli\u003eShoaib MA, Lai KW, Chuah JH, et al. (2022) Comparative studies of deep learning segmentation models for left ventricle segmentation. Front Public Health 10:981019; doi: 10.3389/fpubh.2022.981019. \u003c/li\u003e\n\u003cli\u003eLundberg S, Lee S (2017) A unified approach to interpreting model predictions. Presented at: 31st International Conference on Neural Information Processing Systems; December 4-9, 2017; Long Beach, California, USA.\u003c/li\u003e\n\u003cli\u003eAbou-Elela A (2017) Epidemiology, pathophysiology, and management of uric acid urolithiasis: A narrative review. J Adv Res 8:513-527; doi: 10.1016/j.jare.2017.04.005.\u003c/li\u003e\n\u003cli\u003eBreda A, Territo A, Lopez-Martinez JM (2016) Benefits and risks of ureteral access sheaths for retrograde renal access. Curr Opin Urol 26:70-75; doi: 10.1097/MOU.0000000000000233.\u003c/li\u003e\n\u003cli\u003eQin L, Zhou J, Hu W, et al. (2022) The combination of mean and maximum Hounsfield Unit allows more accurate prediction of uric acid stones. Urolithiasis 50:589-597; doi: 10.1007/s00240-022-01333-2.\u003c/li\u003e\n\u003cli\u003eTailly T, Larish Y, Nadeau B, et al. (2016) Combining Mean and Standard Deviation of Hounsfield Unit Measurements from Preoperative CT Allows More Accurate Prediction of Urinary Stone Composition Than Mean Hounsfield Units Alone. J Endourol 30:453-459; doi: 10.1089/end.2015.0209.\u003c/li\u003e\n\u003cli\u003eNakhostin D, Sartoretti T, Eberhard M, et al. (2021) Low-dose dual-energy CT for stone characterization: a systematic comparison of two generations of split-filter single-source and dual-source dual-energy CT. Abdom Radiol (NY) 46:2079-2089; doi: 10.1007/s00261-020-02852-5.\u003c/li\u003e\n\u003cli\u003eSteyerberg EW, Vickers AJ, Cook NR, et al. (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21:128-138; doi: 10.1097/EDE.0b013e3181c30fb2.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"urolithiasis","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ures","sideBox":"Learn more about [Urolithiasis](http://link.springer.com/journal/240)","snPcode":"240","submissionUrl":"https://submission.nature.com/new-submission/240/3","title":"Urolithiasis","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"decision support techniques, machine learning, urolithiasis, validation","lastPublishedDoi":"10.21203/rs.3.rs-3133615/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3133615/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe correct diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical intervention and be offered alkalization therapy. We developed and validated a machine learning (ML)-based model to identify UA stones from non-UA stones. An international, multicenter study was performed on 202 patients who received percutaneous nephrolithotomy for kidney stones with HU\u0026thinsp;\u0026lt;\u0026thinsp;800. Data from 156 (77.2%) patients were used for model development, while data from 46 (22.8%) patients from a multinational institution were used for external validation. A total of 21,074 kidney and stone contour-annotated computed tomography images were trained with the ResNet-18 Mask R-convolutional neural network algorithm. Finally, this model was concatenated with demographic and clinical data as a fully-connected layer for stone classification. Our model was 100% sensitive in detecting kidney stones in each patient, and the delineation of kidney and stone contours was precise within clinically acceptable ranges. The development model provided an accuracy of 99.9%, with 100.0% sensitivity and 98.9% specificity, in distinguishing UA from non-UA stones. On external validation, the model performed with an accuracy of 97.1%, with 89.4% sensitivity and 98.6% specificity. SHAP plots revealed stone density, diabetes mellitus, and urinary pH as the most important features for classification. Our ML-based model accurately identified and delineated kidney stones and classified UA stones from non-UA stones with the highest predictive accuracy reported to date. Our model can be reliably used to select candidates for an earlier-directed alkalization therapy.\u003c/p\u003e","manuscriptTitle":"Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units \u0026lt;800","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-06 14:32:34","doi":"10.21203/rs.3.rs-3133615/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-30T07:53:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-26T15:19:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10c7d1d0-7434-4577-aa59-20863b984cd1","date":"2023-07-18T00:07:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-15T00:21:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-07-03T06:27:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-03T06:27:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Urolithiasis","date":"2023-07-02T21:44:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"urolithiasis","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ures","sideBox":"Learn more about [Urolithiasis](http://link.springer.com/journal/240)","snPcode":"240","submissionUrl":"https://submission.nature.com/new-submission/240/3","title":"Urolithiasis","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7ec6543a-f2cc-4b53-b755-a0e764e3b901","owner":[],"postedDate":"July 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-02T15:05:10+00:00","versionOfRecord":{"articleIdentity":"rs-3133615","link":"https://doi.org/10.1007/s00240-023-01490-y","journal":{"identity":"urolithiasis","isVorOnly":false,"title":"Urolithiasis"},"publishedOn":"2023-09-30 15:01:52","publishedOnDateReadable":"September 30th, 2023"},"versionCreatedAt":"2023-07-06 14:32:34","video":"","vorDoi":"10.1007/s00240-023-01490-y","vorDoiUrl":"https://doi.org/10.1007/s00240-023-01490-y","workflowStages":[]},"version":"v1","identity":"rs-3133615","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3133615","identity":"rs-3133615","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}