AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening.
Authors
Affiliations (14)
Affiliations (14)
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xian, China.
- Department of Medical Ultrasonics, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangzhou Medical university, Guangzhou, China.
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
- Department of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
- Department of Medical Ultrasonics, Sanshui District People's Hospital, Foshan, China.
- Department of Medical Ultrasound, West China Xiamen Hospital of Sichuan University, Xiamen, China.
- Department of Medical Ultrasonics, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
- School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xian, China. [email protected].
- School of Mechanical Engineering, Tongji University, Shanghai, China. [email protected].
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. [email protected].
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. [email protected].
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, MedAI Collaborative Lab, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. [email protected].
Abstract
Hepatocellular carcinoma (HCC) ultrasound screening encounters challenges related to accuracy and the workload of radiologists. This retrospective, multicenter study assessed four artificial intelligence (AI) enhanced strategies using 21,934 liver ultrasound images from 11,960 patients to improve HCC ultrasound screening accuracy and reduce radiologist workload. UniMatch was used for lesion detection and LivNet for classification, trained on 17,913 images. Among the strategies tested, Strategy 4, which combined AI for initial detection and radiologist evaluation of negative cases in both detection and classification phases, outperformed others. It not only matched the high sensitivity of original algorithm (0.956 vs. 0.991) but also improved specificity (0.787 vs. 0.698), reduced radiologist workload by 54.5%, and decreased both recall and false positive rates. This approach demonstrates a successful model of human-AI collaboration, not only enhancing clinical outcomes but also mitigating unnecessary patient anxiety and system burden by minimizing recalls and false positives.