AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening

作者：Rui-Fang Lu, Chao-Yin She, Dan-Ni He, Mei-Qing Cheng, Ying Wang, Hui Huang, Ya-Dan Lin, Jiayi Lv, Si Qin, Ze-Zhi Liu, Zhiqiang Lu, Wei-Ping Ke, Chaoqun Li, Han Xiao, Zuo‐Feng Xu, Guangjian Liu, Hong Yang, Jie Ren, Haibo Wang, Ming‐De Lu, Qinghua Huang, Li-Da Chen, Wei Wang, Ming Kuang · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-01892-9 · 被引用次数：10 · 研究领域：Radiomics and Machine Learning in Medical Imaging、AI in cancer detection、Artificial Intelligence in Healthcare

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.