This study aims to evaluate the performance of artificial intelligence (AI) in delineating the prostate gland from MRI images by comparing it with contours drawn by diagnostic radiologists, in order to support the accuracy of MRI fusion biopsy for prostate cancer.

This retrospective study developed and evaluated an AI-based prostate segmentation model using 125 annotated prostate MRI cases (3,193 images) from a public dataset for training, and tested it on 109 clinical cases (2,952 images) from the National Cancer Institute of Thailand. The model combined a YOLO-based bounding box detection with the Segment Anything Model (SAM) for prostate segmentation. Model performance was compared to radiologist-drawn contours using Dice Similarity Coefficient (DSC) and % Relative Percent Difference (RPD) in prostate volume estimation

For cases not requiring post-processing, the AI model achieved a mean DSC of 0.72 and an RPD of 8.90% compared to radiologist contours. For cases requiring post-processing, the DSC dropped to 0.66 and the RPD increased to 13.45%. These results indicate a high level of agreement between the AI and expert annotations, particularly in standard cases.

The AI-based model demonstrated promising accuracy in segmenting the prostate gland on MRI scans, comparable to radiologist performance. This approach has the potential to reduce diagnostic delays and lessen radiologist workload in prostate cancer workflows. Future improvements should focus on enhancing model precision, incorporating PIRADS scoring, and validating the system across diverse clinical settings to support safe and effective integration into routine diagnostic practice.