Abstract Objective This study aimed to evaluate the diagnostic accuracy of artificial intelligence (AI)–enhanced micro‐ultrasound (micro‐US) for detecting clinically significant prostate cancer (csPCa) in men referred for prostate biopsy. Patients and Methods We retrospectively analysed 145 men undergoing micro‐US‐guided biopsy (79 with csPCa, 66 without). Deep features were extracted from 2D micro‐US slices using a self‐supervised convolutional autoencoder and classified with a random forest model under fivefold cross‐validation. Patients were considered csPCa‐positive if ≥8 consecutive slices were predicted positive. Diagnostic performance was assessed against biopsy pathology using receiver operating characteristic (ROC) analysis. Results The AI–micro‐US 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‐specific antigen (PSA), digital rectal examination (DRE), age, and prostate volume (AUC 0.753; sensitivity 96.2%, specificity 27.3%). Conclusion AI‐enhanced micro‐US reduces false positives from conventional screening tools while preserving high sensitivity. It shows promise as a point‐of‐care alternative to MRI, integrating risk stratification and biopsy guidance into a single platform.
Imran et al. (Sun,) studied this question.