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

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 Jiang, Jianjun Zheng, Kelei Zhu, Wei Zhou, Baochun Lu, Hongkun Zhou, Yiyu Shen, Jinlin Du, Mingliang Ying, Qiang Hong, Jingang Mo, Jianfeng Li, Guanxiong Ye, Shizheng Zhang, Hongjie Hu, Jihong Sun, Hui Liu, Yiming Li, Xingxin Xu, Huiping Bai, Shuxin Wang, Xin Cheng, Xiaoyin Xu (), Long Jiao (), Risheng Yu (), Wan Yee Lau (), Yizhou Yu () and Xiujun Cai ()
Additional contact information
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

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-45325-9

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