Automated deep learning for real-time focal liver lesions detection in ultrasound videos a multicenter study.
Authors
Affiliations (6)
Affiliations (6)
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
- Yizhun Medical AI Co. Ltd, Beijing, China.
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang Province, China.
- Department of Ultrasound, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China.
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China. [email protected].
Abstract
Early detection of focal liver lesions (FLLs) is crucial for clinical practice, but ultrasound performance heavily depends on operator experience. We developed Auto-DFLLs, an automated deep learning model based on ResNet and FPN architectures to detect FLLs in ultrasound videos. It was trained and validated on 5258 prospectively collected videos from three hospitals. On internal validation, Auto-DFLLs achieved an AP50 (average precision at IoU = 50%) of 0.7772, Pr70 (precision at 70% recall) of 0.7967, and FP70 (false positives at 70% recall) of 3.4688. Validation study showed that Auto-DFLLs significantly improved junior sonographers' detection (AFROC-AUC: 79.52 vs. 71.55, P = 0.021) and enhanced senior sonographers' performance (AFROC-AUC: 78.64 vs. 74.57, P = 0.0366), especially for small lesions (< 10 mm, P = 0.034). Auto-DFLLs maintained stable detection across different lesion size, echogenicity, location, and ultrasound equipment from different manufacturers. Auto-DFLLs reduces operator-dependent variability and offers a reliable assistive tool for real-time FLLs screening, particularly valuable in resource-limited areas.