{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang X"],"funding":["Key Research and Development Projects of Shaanxi Province","Natural Science Foundation of China","Natural Science Foundation of Shaanxi Province, China","Shaanxi Provincial Innovation","National Natural Science Foundation of China","Shaanxi Provincial Key Industry Innovation Chain"],"pagination":["614"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12533055"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8(1)"],"pubmed_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"],"journal":["NPJ digital medicine"],"pubmed_title":["A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow."],"pmcid":["PMC12533055"],"funding_grant_id":["2024JC-ZDXM-52","2024SF-ZDCYL-01-17","2023PT-07","82172993","82172860","82103037","2022ZDLSF04-04"],"pubmed_authors":["Yang H","Zou L","Zhang J","Zhu Y","Liu M","Lv X","Ai L","Wang J","Shi X","Wang L","Liu X","Zhao M","Zhao L","Chen Z","Zhao R","Zhang X","Gao Y","Wang X","Zhang B"],"additional_accession":[]},"is_claimable":false,"name":"A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow.","description":"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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-04T14:25:49.532Z","creation":"2026-05-10T03:11:26.623Z"},"accession":"S-EPMC12533055","cross_references":{"pubmed":["41102390"],"doi":["10.1038/s41746-025-01986-4"]}}