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Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800
DC Field | Value | Language |
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dc.contributor.author | 구교철 | - |
dc.date.accessioned | 2024-01-03T01:46:16Z | - |
dc.date.available | 2024-01-03T01:46:16Z | - |
dc.date.issued | 2023-09 | - |
dc.identifier.issn | 2194-7228 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/197656 | - |
dc.description.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 stones on computed tomography (CT) images and simultaneously classify 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 CT 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. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Springer | - |
dc.relation.isPartOf | UROLITHIASIS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Algorithms | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Kidney Calculi* / diagnostic imaging | - |
dc.subject.MESH | Kidney Calculi* / surgery | - |
dc.subject.MESH | Nephrolithotomy, Percutaneous* | - |
dc.subject.MESH | Tomography, X-Ray Computed / methods | - |
dc.subject.MESH | Uric Acid | - |
dc.subject.MESH | Urinary Calculi* | - |
dc.title | Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800 | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Urology (비뇨의학교실) | - |
dc.contributor.googleauthor | Ben H Chew | - |
dc.contributor.googleauthor | Victor K F Wong | - |
dc.contributor.googleauthor | Abdulghafour Halawani | - |
dc.contributor.googleauthor | Sujin Lee | - |
dc.contributor.googleauthor | Sangyeop Baek | - |
dc.contributor.googleauthor | Hoyong Kang | - |
dc.contributor.googleauthor | Kyo Chul Koo | - |
dc.identifier.doi | 10.1007/s00240-023-01490-y | - |
dc.contributor.localId | A00188 | - |
dc.relation.journalcode | J02939 | - |
dc.identifier.eissn | 2194-7236 | - |
dc.identifier.pmid | 37776331 | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s00240-023-01490-y | - |
dc.subject.keyword | Decision support techniques | - |
dc.subject.keyword | Machine learning | - |
dc.subject.keyword | Urolithiasis | - |
dc.subject.keyword | Validation | - |
dc.contributor.alternativeName | Koo, Kyo Chul | - |
dc.contributor.affiliatedAuthor | 구교철 | - |
dc.citation.volume | 51 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 117 | - |
dc.identifier.bibliographicCitation | UROLITHIASIS, Vol.51(1) : 117, 2023-09 | - |
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