A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow.

ABSTRACT: Accurate ovarian cancer screening and diagnosis are critical for patient survival. We present UMORSS, an AI-assisted diagnostic system integrating ultrasound (US) imaging and clinical data with uncertainty quantification for precise ovarian cancer risk assessment. Developed and evaluated using a multicentre dataset (7352 patients, 7594 lesions, 9281 US images), UMORSS employs a two-phase approach: Phase I rapidly triages low-risk lesions via initial US analysis, and Phase II provides uncertainty-aware multimodal analysis for complex cases. Phase I accurately identified 68.7% of physiological cysts and 13.8% of benign tumours as low-risk, with zero false negatives, and Phase II achieved an AUC of 0.955 (internal testing) and 0.926 (external validation). Furthermore, a prospective reader stu

SUBMITTER: Wang X 

PROVIDER: S-EPMC12533055 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature