CT-based artificial intelligence system complementing deep learning model and radiologist for liver fibrosis staging

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.

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Publication Details

Journal
iScience
Published
2025-03-17
DOI
https://doi.org/10.1016/j.isci.2025.112224
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
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article

CT-based artificial intelligence system complementing deep learning model and radiologist for liver fibrosis staging

Wei Jiang, Kan He, Wenao Ma, Huimao Zhang et al.
1 citations
iScience
Radiomics and Machine Learning in Medical Imaging
1.36
article

CT-based artificial intelligence system complementing deep learning model and radiologist for liver fibrosis staging

Wei Jiang, Kan He, Wenao Ma, Huimao Zhang, Hyun O. Lee, Li-Ming Zhao, Qi Dou, Shunlin Guo, Jia Liu, Jinzhen Cao, Pujun Gao, Tiffany Y. So, Lei Zhang, Lei Wang, Mingyang Li, Long-Lin Yin, Shuang Zheng, Zhai Yanan, Junqi Niu, Lin Mu
article en
1 citations

Abstract

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.

iScienceVol. 28(4)
Chinese University of Hong Kong (HK), Jilin University (CN), National Health and Family Planning Commission (CN), Prince of Wales Hospital (CN), Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (CN), First Hospital of Jilin University (CN), Princess Margaret Hospital (HK), First Hospital of Lanzhou University (CN), Lanzhou University (CN)
Openalex Percentile: Top 22%
Radiomics and Machine Learning in Medical Imaging
1.36
DOIOpen Access PDFOpenAlex
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