CT-based artificial intelligence system complementing deep learning model and radiologist for liver fibrosis staging - Shuang Zheng; Wenao Ma; Lin Mu; Kan He; Jianfeng Cao; Tiffany Y. So; Lei Zhang; Mingyang Li; Yanan Zhai; Feng Liu; Shunlin Guo; Longlin Yin; et al. (iScience)
Summary: Noninvasive methods for liver fibrosis staging are urgently needed due to its significance in predicting significant morbidity and mortality. In this study, we developed an automated DL-based segmentation and classification model (Model-C). Test-time adaptation was used to address data distribution shifts. We then established a deep learning-radiologist complementarity decision system (DRCDS) via a decision model determining whether to adopt Model-C’s diagnosis or defer to radiologists. Model-C (AUCs of 0.89–0.92) outperformed models based on liver (AUCs: 0.84–0.90) or spleen (AUCs: 0.69–0.70). With test-time adaptation, the Obuchowski index values of Model-C in three external sets improved from 0.81, 0.73, and 0.73 to 0.85, 0.85, and 0.81. DRCDS performed slightly better than Model-C or senior radiologists, with 73.7%–92.0% of cases adopting Model-C’s diagnosis. In conclusion, DRCDS could diagnose liver fibrosis with high accuracy. Additionally, we provided solutions to model generalization and human-machine complementarity issues in multi-classification problems.