{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:33:09Z","timestamp":1787027589692,"version":"build-2736575974"},"reference-count":72,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T00:00:00Z","timestamp":1692144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Kidney\u2013ureter\u2013bladder (KUB) imaging is used as a frontline investigation for patients with suspected renal stones. In this study, we designed a computer-aided diagnostic system for KUB imaging to assist clinicians in accurately diagnosing urinary tract stones. The image dataset used for training and testing the model comprised 485 images provided by Kaohsiung Chang Gung Memorial Hospital. The proposed system was divided into two subsystems, 1 and 2. Subsystem 1 used Inception-ResNetV2 to train a deep learning model on preprocessed KUB images to verify the improvement in diagnostic accuracy with image preprocessing. Subsystem 2 trained an image segmentation model using the ResNet hybrid, U-net, to accurately identify the contours of renal stones. The performance was evaluated using a confusion matrix for the classification model. We conclude that the model can assist clinicians in accurately diagnosing renal stones via KUB imaging. Therefore, the proposed system can assist doctors in diagnosis, reduce patients\u2019 waiting time for CT scans, and minimize the radiation dose absorbed by the body.<\/jats:p>","DOI":"10.3390\/bioengineering10080970","type":"journal-article","created":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T10:09:33Z","timestamp":1692180573000},"page":"970","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Design and Validation of a Deep Learning Model for Renal Stone Detection and Segmentation on Kidney\u2013Ureter\u2013Bladder Images"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9422-271X","authenticated-orcid":false,"given":"Zih-Hao","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5143-9505","authenticated-orcid":false,"given":"Yi-Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan"},{"name":"Department of Urology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Juei","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0853-3584","authenticated-orcid":false,"given":"Ko-Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,16]]},"reference":[{"key":"ref_1","first-page":"e86","article-title":"Kidney stones: A global picture of prevalence, incidence, and associated risk factors","volume":"12","author":"Romero","year":"2010","journal-title":"Rev. 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