Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric.

ABSTRACT:

Objectives

This study compared two uncertainty quantification (UQ) metrics to rule out prostate MRI scans with a high-confidence artificial intelligence (AI) prediction and investigated the resulting potential radiologist's workload reduction in a clinically significant prostate cancer (csPCa) detection pathway.

Materials and methods

This retrospective study utilized 1612 MRI scans from three institutes for csPCa (Gleason Grade Group ≥ 2) assessment. We compared the standard diagnostic pathway (radiologist reading) to an AI-based rule-out pathway in terms of efficacy and accuracy in diagnosing csPCa. In the rule-out pathway, 15 AI submodels (trained on 7756 cases) diagnosed each MRI scan, and any prediction deemed uncertain was referred to a radiologist for reading. We compa

SUBMITTER: Fransen SJ 

PROVIDER: S-EPMC12634809 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature