{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:54:26Z","timestamp":1790297666994,"version":"4.1.0"},"reference-count":18,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":4,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJUI Compass"],"published-print":{"date-parts":[[2026,2]]},"abstract":"Abstract<\/jats:title>\n \n Objective<\/jats:title>\n This study aimed to evaluate the diagnostic accuracy of artificial intelligence (AI)\u2013enhanced micro\u2010ultrasound (micro\u2010US) for detecting clinically significant prostate cancer (csPCa) in men referred for prostate biopsy.<\/jats:p>\n <\/jats:sec>\n \n Patients and Methods<\/jats:title>\n We retrospectively analysed 145 men undergoing micro\u2010US\u2010guided biopsy (79 with csPCa, 66 without). Deep features were extracted from 2D micro\u2010US slices using a self\u2010supervised convolutional autoencoder and classified with a random forest model under fivefold cross\u2010validation. Patients were considered csPCa\u2010positive if \u22658 consecutive slices were predicted positive. Diagnostic performance was assessed against biopsy pathology using receiver operating characteristic (ROC) analysis.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The AI\u2013micro\u2010US model achieved an area under the ROC curve (AUC) of 0.871. At a fixed threshold, sensitivity was 92.5% and specificity 68.1%, outperforming a clinical model based on prostate\u2010specific antigen (PSA), digital rectal examination (DRE), age, and prostate volume (AUC 0.753; sensitivity 96.2%, specificity 27.3%).<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n AI\u2010enhanced micro\u2010US reduces false positives from conventional screening tools while preserving high sensitivity. It shows promise as a point\u2010of\u2010care alternative to MRI, integrating risk stratification and biopsy guidance into a single platform.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/bco2.70133","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T06:48:46Z","timestamp":1770360526000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["AI\u2010enhanced micro\u2010ultrasound improves detection of clinically significant prostate cancer at biopsy"],"prefix":"10.1002","volume":"7","author":[{"given":"Muhammad","family":"Imran","sequence":"first","affiliation":[{"name":"School of Data Science & Analytics Kennesaw State University Marietta Georgia USA"},{"name":"Department of Medicine University of Florida Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0470-5262","authenticated-orcid":false,"given":"Wayne 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