ArticleNPJ digital medicine2025
A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Development and validation of an artificial intelligence-based model for diagnosing benign, borderline, and malignant adnexal masses.NPJ precision oncology · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Integration of serological markers with O-RADS ultrasound risk stratification achieves high diagnostic accuracy for early ovarian cancer: development and validation of an interpretable model.American journal of cancer research · 2026Article
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.