CNN-Certainty-Directed Utilization of Deep Learning in Radiological Assessment of Adnexal Masses on Pelvic MRI

Motivation: Multi-parametric pelvic MRI for evaluation of adnexal masses is currently assessed using the O-RADS system. AI-driven approaches using convolutional neural networks (CNN) require further evaluation. Goal(s): Determine if methods to infer the uncertainty of CNN assessment provide guidance for utilization of fully-automated CNN-based assessment of pelvic MRI to partily substitute for experienced radiologist assessment and for enhanced efficiency of the diagnostic pathway. Approach: Construct a CNN ensemble for the segmentation and classification of adnexal masses to stratify CNN predictions by CNN prediction certaintly. Results: A proportion of cases can be assessed by CNN only with no or only minimal reduction in diagnostic accuracy. Impact: CNN-based triage of multi-parametric pelvic MRI for assessment of adnexal masses has potential to support radiological decision making.