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An artificial intelligence platform for the multihospital collaborative management of congenital cataracts

Nature Biomedical Engineering volume 1, Article number: 0024 (2017) Cite this article

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Abstract

Using artificial intelligence (AI) to prevent and treat diseases is an ultimate goal in computational medicine. Although AI has been developed for screening and assisted decision-making in disease prevention and management, it has not yet been validated for systematic application in the clinic. In the context of rare diseases, the main strategy has been to build specialized care centres; however, these centres are scattered and their coverage is insufficient, which leaves a large proportion of rare-disease patients with inadequate care. Here, we show that an AI agent using deep learning, and involving convolutional neural networks for diagnostics, risk stratification and treatment suggestions, accurately diagnoses and provides treatment decisions for congenital cataracts in an in silico test, in a website-based study, in a ‘finding a needle in a haystack’ test and in a multihospital clinical trial. We also show that the AI agent and individual ophthalmologists perform equally well. Moreover, we have integrated the AI agent with a cloud-based platform for multihospital collaboration, designed to improve disease management for the benefit of patients with rare diseases.

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Figure 1: The functional architecture and training pipeline for CC-Cruiser.
Figure 2: The proportion of accurate diagnoses, false positives and missed detections in CC-Cruiser.
Figure 3: Comparison of the performance of the CC-Cruiser agent and individual ophthalmologists.
Figure 4: Cloud-based multihospital AI platform.

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Acknowledgements

The authors acknowledge the involvement of the China Rare-disease Medical Intelligence (CRMI) Collaboration, which currently consists of Zhongshan Ophthalmic Centre (H.L., E.L. and Y.L.), Guangdong General Hospital (J. Zeng), Qingyuan People’s Hospital (Y. Lu) and the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine (X. Yu). The CRMI Collaboration may be extended with more participating hospitals in the future. This study was funded by the 973 Program (2015CB964600), the NSFC (91546101), the Guangdong Provincial Natural Science Foundation for Distinguished Young Scholars of China (2014A030306030), the Youth Pearl River Scholar Funded Scheme (H.L., 2016), and the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase).

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Author notes

  1. Erping Long and Haotian Lin: These authors contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, 510060, China

    Erping Long, Haotian Lin, Zhenzhen Liu, Xiaohang Wu, Zhuoling Lin, Xiaoyan Li, Jingjing Chen, Jing Li, Qianzhong Cao, Dongni Wang, Weirong Chen & Yizhi Liu

  2. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Liming Wang, Jiewei Jiang, Yingying An & Xiyang Liu

  3. School of Software, Xidian University, Xi’an 710071, China.,

    Xiyang Liu

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Contributions

H.L., E.L., X.Liu and Y.L. designed the research; E.L., H.L., J.J., Z.Liu, X.W., L.W., Z.Lin, X.Li, J.C., J.L., Q.C., D.W. and W.C. conducted the study; E.L., H.L. and J.J. collected the data; E.L., H.L., J.J. and Y.A. analysed the data; E.L. and H.L. co-wrote the manuscript; all authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Haotian Lin or Yizhi Liu.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary algorithm information, figures, tables and test paper. (PDF 721 kb)

Supplementary Video 1

Instructions for using the CC-Cruiser website. (MP4 30017 kb)

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Long, E., Lin, H., Liu, Z. et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng 1, 0024 (2017). https://doi.org/10.1038/s41551-016-0024

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