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Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography
  1. Bing Li1,2,
  2. Huan Chen1,2,
  3. Bilei Zhang1,2,
  4. Mingzhen Yuan3,
  5. Xuemin Jin4,
  6. Bo Lei5,
  7. Jie Xu3,
  8. Wei Gu6,
  9. David Chuen Soong Wong7,
  10. Xixi He8,
  11. Hao Wang8,
  12. Dayong Ding8,
  13. Xirong Li9,
  14. Youxin Chen1,2,
  15. Weihong Yu1,2
  1. 1Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
  2. 2Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Peking Union Mecical College, Beijing, China
  3. 3Department of Ophthalmology, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
  4. 4Department of Ophthalmology, Zhengzhou University First Affiliated Hospital, Zhengzhou, Henan, China
  5. 5Clinical Research Center, Henan Eye Institute, Henan Eye Hospital, Clinical Research Center, Henan Provincial People's Hospital, Zhengzhou, Henan, China
  6. 6Department of Ophthalmology, Beijing Aier Intech Eye Hospital, Beijing, China
  7. 7University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, UK
  8. 8Vistel AI Lab, Visionary Intelligence Ltd, Beijing, China
  9. 9Key Lab of DEKE, Renmin University of China, Beijing, China
  1. Correspondence to Dr Weihong Yu; 536273640{at}qq.com; Professor Youxin Chen, Ophthalmology, Peking Union Medical College Hospital, Beijing, China; chenyx{at}pumch.cn

Abstract

Aim To explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography.

Methods Diagnostic performance of a DLS was tested on the detection of normal fundus and 12 major fundus diseases including referable diabetic retinopathy, pathologic myopic retinal degeneration, retinal vein occlusion, retinitis pigmentosa, retinal detachment, wet and dry age-related macular degeneration, epiretinal membrane, macula hole, possible glaucomatous optic neuropathy, papilledema and optic nerve atrophy. The DLS was developed with 56 738 images and tested with 8176 images from one internal test set and two external test sets. The comparison with human doctors was also conducted.

Results The area under the receiver operating characteristic curves of the DLS on the internal test set and the two external test sets were 0.950 (95% CI 0.942 to 0.957) to 0.996 (95% CI 0.994 to 0.998), 0.931 (95% CI 0.923 to 0.939) to 1.000 (95% CI 0.999 to 1.000) and 0.934 (95% CI 0.929 to 0.938) to 1.000 (95% CI 0.999 to 1.000), with sensitivities of 80.4% (95% CI 79.1% to 81.6%) to 97.3% (95% CI 96.7% to 97.8%), 64.6% (95% CI 63.0% to 66.1%) to 100% (95% CI 100% to 100%) and 68.0% (95% CI 67.1% to 68.9%) to 100% (95% CI 100% to 100%), respectively, and specificities of 89.7% (95% CI 88.8% to 90.7%) to 98.1% (95%CI 97.7% to 98.6%), 78.7% (95% CI 77.4% to 80.0%) to 99.6% (95% CI 99.4% to 99.8%) and 88.1% (95% CI 87.4% to 88.7%) to 98.7% (95% CI 98.5% to 99.0%), respectively. When compared with human doctors, the DLS obtained a higher diagnostic sensitivity but lower specificity.

Conclusion The proposed DLS is effective in diagnosing normal fundus and 12 major fundus diseases, and thus has much potential for fundus diseases screening in the real world.

  • imaging
  • retina
  • diagnostic tests/investigation

Data availability statement

Data are available upon reasonable request.

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Data availability statement

Data are available upon reasonable request.

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Footnotes

  • WY and YC contributed equally.

  • Contributors BL contributed to the statistical analysis, drafting and revising of the manuscript. HC, BZ and MY contributed to the standard operating procedure and quality control of the datasets. XJ, BL, JX and WG contributed to the acquisition of the color fundus photograph of the datasets. DCSW contributed to the revision of the manuscript. XH and HW contributed to the models’ developing, statistical analysis and preparing of the figures for the work. XL and DD contributed to the development of the models and interpretation of data, and revision of the manuscript for this study. YC and WY contributed to the conception and design of the work, revision of the manuscript and will final approval of the version to be published.

  • Funding CAMS Initiative for Innovative Medicine (CAMS-12M)(2018-I2M-AI-001). Pharmaceutical collaborative innovation research project of Beijing Science and Technology Commission (Z191100007719002). Beijing Natural Science Foundation Haidian original innovation joint fund (19L2062). Natural Science Foundation of Beijing Municipality 4202033. The priming scientific research foundation for the junior researcher in Beijing Tongren Hospital, Capital Medical University (2018-YJJ-ZZL-052).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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