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Artificial intelligence in liver imaging: methods and applications

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Abstract

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.

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All data used in this review is publicly available.

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Funding

This work is supported by research grants from the National Natural Science Foundation of China [T2341008, 81225025 and 82305047] and sponsored by Tsinghua-Toyota Joint Research Fund, and Anhui Province Traditional Chinese Medicine Science and Technology Research Project [202303a07020001].

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  1. Peng Zhang, Chaofei Gao and Yifei Huang should be regarded as the joint first authors.

Authors and Affiliations

  1. Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China

    Peng Zhang, Chaofei Gao, Xiangyi Chen, Zhuoshi Pan, Lan Wang & Shao Li

  2. Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Southeast University, Nanjing, China

    Xiaolong Qi

  3. Department of Gastroenterology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Yifei Huang

  4. CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Di Dong

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Zhang, P., Gao, C., Huang, Y. et al. Artificial intelligence in liver imaging: methods and applications. Hepatol Int 18, 422–434 (2024). https://doi.org/10.1007/s12072-023-10630-w

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