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A multicenter clinical AI system study for detection and diagnosis of focal liver lesions

Author

Listed:
  • Hanning Ying

    (Zhejiang University School of Medicine)

  • Xiaoqing Liu

    (Deepwise Artificial Intelligence Laboratory)

  • Min Zhang

    (Zhejiang University)

  • Yiyue Ren

    (Zhejiang University)

  • Shihui Zhen

    (Zhejiang University)

  • Xiaojie Wang

    (Zhejiang University)

  • Bo Liu

    (Deepwise Artificial Intelligence Laboratory)

  • Peng Hu

    (Zhejiang University School of Medicine)

  • Lian Duan

    (Zhejiang University School of Medicine)

  • Mingzhi Cai

    (Zhangzhou Municipal Hospital of Fujian Province)

  • Ming Jiang

    (Quzhou People’s Hospital)

  • Xiangdong Cheng

    (Cancer Hospital of the University of Chinese Academy of Sciences (ZheJiang Cancer Hospital))

  • Xiangyang Gong

    (Zhejiang Provincial People’s Hospital)

  • Haitao Jiang

    (Cancer Hospital of the University of Chinese Academy of Sciences (ZheJiang Cancer Hospital))

  • Jianshuai Jiang

    (Ningbo First Hospital)

  • Jianjun Zheng

    (University of Chinese Academy of Sciences (Ningbo No.2 Hospital))

  • Kelei Zhu

    (Yinzhou People’s Hospital)

  • Wei Zhou

    (Affiliated Central Hospital of Huzhou University)

  • Baochun Lu

    (Shaoxing People’s Hospital)

  • Hongkun Zhou

    (The First Hospital of Jiaxing Affiliated Hospital of Jiaxing University)

  • Yiyu Shen

    (The Second Hospital of Jiaxing Affiliated Hospital of Jiaxing University)

  • Jinlin Du

    (Jinhua Municipal Central Hospital)

  • Mingliang Ying

    (Jinhua Municipal Central Hospital)

  • Qiang Hong

    (Jinhua GuangFU Hospital)

  • Jingang Mo

    (Taizhou Municipal Central Hospital)

  • Jianfeng Li

    (The First People’s Hospital of Wenling)

  • Guanxiong Ye

    (Lishui People’s Hospital)

  • Shizheng Zhang

    (Zhejiang University School of Medicine)

  • Hongjie Hu

    (Zhejiang University School of Medicine)

  • Jihong Sun

    (Zhejiang University School of Medicine)

  • Hui Liu

    (Zhejiang University School of Medicine)

  • Yiming Li

    (Deepwise Artificial Intelligence Laboratory)

  • Xingxin Xu

    (Deepwise Artificial Intelligence Laboratory)

  • Huiping Bai

    (Deepwise Artificial Intelligence Laboratory)

  • Shuxin Wang

    (Deepwise Artificial Intelligence Laboratory)

  • Xin Cheng

    (Xiamen University)

  • Xiaoyin Xu

    (Harvard Medical School)

  • Long Jiao

    (Imperial College London)

  • Risheng Yu

    (Second Affiliated Hospital of Zhejiang University School of Medicine)

  • Wan Yee Lau

    (the Chinese University of Hong Kong)

  • Yizhou Yu

    (Department of Computer Science, The University of Hong Kong)

  • Xiujun Cai

    (Zhejiang University School of Medicine)

Abstract

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists’ F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

Suggested Citation

  • Hanning Ying & Xiaoqing Liu & Min Zhang & Yiyue Ren & Shihui Zhen & Xiaojie Wang & Bo Liu & Peng Hu & Lian Duan & Mingzhi Cai & Ming Jiang & Xiangdong Cheng & Xiangyang Gong & Haitao Jiang & Jianshuai, 2024. "A multicenter clinical AI system study for detection and diagnosis of focal liver lesions," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45325-9
    DOI: 10.1038/s41467-024-45325-9
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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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