A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow.
Authors
Affiliations (13)
Affiliations (13)
- School of Computer Science and Technology, Xidian University, Xi'an, China.
- Department of Gynecology and Obstetrics, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
- Department of Gynecology and Obstetrics, Xi'an People's Hospital, Xi'an Fourth Hospital, Xi'an, China.
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- Department of Gynecology and Obstetrics, Xi'an Aerospace Hospital, Xi'an, China.
- School of Computer Science and Technology, Xidian University, Xi'an, China. [email protected].
- Hangzhou Institute of Technology, Xidian University, Hangzhou, China. [email protected].
- Xi'an Key Laboratory of Medical Artificial Intelligence, Xi'an, China. [email protected].
- School of Computer Science and Technology, Xidian University, Xi'an, China. [email protected].
- Hangzhou Institute of Technology, Xidian University, Hangzhou, China. [email protected].
- Xi'an Key Laboratory of Medical Artificial Intelligence, Xi'an, China. [email protected].
- Department of Gynecology and Obstetrics, Xijing Hospital, Fourth Military Medical University, Xi'an, China. [email protected].
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 study (n = 284 cases, six radiologists) demonstrated that UMORSS as a human-AI collaborative tool increased radiologists' average AUC by 10.58% and sensitivity by 22.48%. UMORSS shows strong potential to streamline clinical workflow, optimize resource allocation, and standardize ovarian cancer diagnosis.