A foundation model for acute abdomen diagnosis stratification and triage on noncontrast computed tomography.
Authors
Affiliations (12)
Affiliations (12)
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Faculty of Medical Imaging Technology, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Radiology, Southeast University Affiliated Nantong First People's Hospital, Nantong, China.
- Department of Radiology, Wuxi No.2 People's Hospital, Jiangnan University Medical Center, Wuxi, China.
- Department of Radiology, Shanghai East Hospital, Tongji University, Shanghai, China.
- Clinical Research Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
- Department of Radiology, Shanghai East Hospital, Tongji University, Shanghai, China. [email protected].
- Department of Radiology, Wuxi No.2 People's Hospital, Jiangnan University Medical Center, Wuxi, China. [email protected].
- Department of Radiology, Southeast University Affiliated Nantong First People's Hospital, Nantong, China. [email protected].
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- Faculty of Medical Imaging Technology, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
Abstract
Accurate and timely diagnosis of acute abdominal emergencies remains challenging. Non-contrast computed tomography (NCCT) is often used as an initial imaging modality because of atypical presentations, contraindications to contrast, or resource constraints. Here we present AbdomenNet, a multi-task AI system built on a self-supervised foundation model that detects 11 acute abdominal conditions and performs three risk-stratification subtasks from NCCT images. AbdomenNet is pre-trained on 103,989 NCCT examinations and fine-tuned on 5816 annotated cases. We assess generalizability in 2528 patients from three independent external cohorts, evaluate radiologist performance in a multi-reader multi-case crossover study, and estimate workflow impact using retrospective reconstruction. In external validation, AbdomenNet achieves a macro-average AUROC of 0.919 for five emergent conditions. AI assistance increases radiologists' mean AUROC from 0.812 to 0.924 and reduces median reading time by 52.5 seconds per case. Workflow reconstruction indicates that AI-driven prioritization could shorten median report turnaround time by 37 minutes.