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Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images

Author

Listed:
  • Wenying Zhou

    (Sun Yat-sen University)

  • Yang Yang

    (Sun Yat-sen University)

  • Cheng Yu

    (Huazhong University of Science and Technology)

  • Juxian Liu

    (Sichuan University)

  • Xingxing Duan

    (Hunan Children’s Hospital)

  • Zongjie Weng

    (Affiliated Hospital of Fujian Medical University)

  • Dan Chen

    (Guangdong Women and Children’ Hospital)

  • Qianhong Liang

    (Hexian Memorial Affiliated Hospital of Southern Medical University)

  • Qin Fang

    (The First People’s Hospital of Foshan)

  • Jiaojiao Zhou

    (Sichuan University)

  • Hao Ju

    (Shengjing Hospital of China Medical University)

  • Zhenhua Luo

    (Sun Yat-sen University)

  • Weihao Guo

    (Sun Yat-sen University)

  • Xiaoyan Ma

    (Guangdong Women and Children’ Hospital)

  • Xiaoyan Xie

    (Sun Yat-sen University)

  • Ruixuan Wang

    (Sun Yat-sen University)

  • Luyao Zhou

    (Sun Yat-sen University)

Abstract

It is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural area without relevant expertise. To help diagnose BA based on sonographic gallbladder images, an ensembled deep learning model is developed. The model yields a patient-level sensitivity 93.1% and specificity 93.9% [with areas under the receiver operating characteristic curve of 0.956 (95% confidence interval: 0.928-0.977)] on the multi-center external validation dataset, superior to that of human experts. With the help of the model, the performances of human experts with various levels are improved. Moreover, the diagnosis based on smartphone photos of sonographic gallbladder images through a smartphone app and based on video sequences by the model still yields expert-level performances. The ensembled deep learning model in this study provides a solution to help radiologists improve the diagnosis of BA in various clinical application scenarios, particularly in rural and undeveloped regions with limited expertise.

Suggested Citation

  • Wenying Zhou & Yang Yang & Cheng Yu & Juxian Liu & Xingxing Duan & Zongjie Weng & Dan Chen & Qianhong Liang & Qin Fang & Jiaojiao Zhou & Hao Ju & Zhenhua Luo & Weihao Guo & Xiaoyan Ma & Xiaoyan Xie & , 2021. "Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21466-z
    DOI: 10.1038/s41467-021-21466-z
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    Cited by:

    1. Kang Su & Jingwei Liu & Xiaoqi Ren & Yingxiang Huo & Guanglong Du & Wei Zhao & Xueqian Wang & Bin Liang & Di Li & Peter Xiaoping Liu, 2024. "A fully autonomous robotic ultrasound system for thyroid scanning," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. Xueyi Zheng & Ruixuan Wang & Xinke Zhang & Yan Sun & Haohuan Zhang & Zihan Zhao & Yuanhang Zheng & Jing Luo & Jiangyu Zhang & Hongmei Wu & Dan Huang & Wenbiao Zhu & Jianning Chen & Qinghua Cao & Hong , 2022. "A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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