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DeepUWF-plus: automatic fundus identification and diagnosis system based on ultrawide-field fundus imaging

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Abstract

Poor eye health is a major public health problem, and the timely detection and diagnosis of fundus abnormalities is important for eye health protection. Traditional imaging models can hinder the comprehensive evaluation of fundus abnormalities due to the use of a narrow field of view. The emerging ultrawide-field (UWF) imaging model surpasses this limitation in a non invasive, wide-view manner and is suitable for fundus observation and screening. Nevertheless, manual screening is labour intensive and subjective, especially in the absence of an ophthalmologist. Therefore, a set of auxiliary screening methods for a fundus screening service using a combination of deep learning and UWF imaging technology, which is designated as DeepUWF-Plus, is proposed. This service includes a subsystem for the screening of fundus, a subsystem for the identification of abnormalities regarding four important fundus locations, and a subsystem for the diagnosis of four retinal diseases that threaten vision. The influence of two-stage and one-stage classification strategies on the prediction performance of the model is experimentally investigated to alleviate severe class imbalance and similarity between classes, and evaluate the effectiveness and reliability of the system. Our experimental results show that DeepUWF-Plus is effective when using the two-stage strategy, especially for identifying signs or symptoms of minor diseases. DeepUWF-Plus can improve the practicality of fundus screening and enable ophthalmologists to provide more comprehensive fundus assessments.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China under Grant 2018AAA0100201, the National Natural Science Foundation of China under Grant 61702349, and the Sichuan Province Science and Technology Support Program 2019YFS0246.

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Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of China

    Wei Zhang, Yuanyuan Chen & Zhang Yi

  2. Mianyang Central Hospital, Mianyang, Sichuan Province, 621000, China

    Yan Dai

  3. School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, China

    Miao Liu & Jie Zhong

  4. The Department of Ophthalmology, Sichuan Academy of Medical Sciences and Sichuan Provincial Peoples Hospital, Chengdu, 610072, China

    Miao Liu & Jie Zhong

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  1. Wei Zhang
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Correspondence to Yuanyuan Chen or Jie Zhong.

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Wei Zhang and Yan Dai are co-first authors

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Zhang, W., Dai, Y., Liu, M. et al. DeepUWF-plus: automatic fundus identification and diagnosis system based on ultrawide-field fundus imaging. Appl Intell 51, 7533–7551 (2021). https://doi.org/10.1007/s10489-021-02242-4

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