AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening
Abstrak
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.
Topik & Kata Kunci
Penulis (24)
Rui-Fang Lu
Chao-Yin She
Dan-Ni He
Mei-Qing Cheng
Ying Wang
Hui Huang
Ya-Dan Lin
Jia-Yi Lv
Si Qin
Ze-Zhi Liu
Zhi-Rong Lu
Wei-Ping Ke
Chao-Qun Li
Han Xiao
Zuo-Feng Xu
Guang-Jian Liu
Hong Yang
Jie Ren
Hai-Bo Wang
Ming-De Lu
Qing-Hua Huang
Li-Da Chen
Wei Wang
Ming Kuang
Akses Cepat
- Tahun Terbit
- 2025
- Sumber Database
- DOAJ
- DOI
- 10.1038/s41746-025-01892-9
- Akses
- Open Access ✓