{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T17:02:57Z","timestamp":1793638977948,"version":"4.5.1"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Kidney stone disease is a common urological condition requiring timely detection to prevent complications. Non-contrast computed tomography (CT) is the gold standard for detecting renal calculi, but manual interpretation is time-consuming and subject to variability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study evaluates four deep learning object detection models\u2014YOLOv8, YOLOv5, Faster R-CNN, and RetinaNet\u2014for automated kidney stone detection in CT images. A dataset of 4,000 annotated CT slices from 170 patients was used. Performance was evaluated using mAP@0.5, precision, recall, false positive and false negative rates, and inference speed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Faster R-CNN achieved the highest localization accuracy (mAP@0.5 = 0.93), while YOLOv8 demonstrated the best balance between accuracy (mAP@0.91) and computational efficiency, achieving real-time inference at 65 FPS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The results highlight the trade-off between detection accuracy and processing speed across architectures. YOLOv8 provides an optimal balance for clinical implementation due to its strong performance and real-time capability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1702159","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T05:39:44Z","timestamp":1773812384000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Urological diagnostics based on kidney stone detection in CT imaging using YOLOv8 deep learning framework"],"prefix":"10.3389","volume":"13","author":[{"given":"Yuguang","family":"Ye","sequence":"first","affiliation":[{"name":"School of Mathematics and Computer Science, Quanzhou Normal University","place":["Quanzhou, China"]},{"name":"Fujian Provincial Key Laboratory of Data-Intensive Computing, Quanzhou Normal University","place":["Quanzhou, China"]},{"name":"Key Laboratory of Intelligent Computing and Information Processing (Quanzhou Normal 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