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Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images
  1. Zhongwen Li1,
  2. Chong Guo1,
  3. Duoru Lin1,
  4. Danyao Nie2,
  5. Yi Zhu3,
  6. Chuan Chen4,
  7. Lanqin Zhao1,
  8. Jinghui Wang1,
  9. Xulin Zhang1,
  10. Meimei Dongye1,
  11. Dongni Wang1,
  12. Fabao Xu1,
  13. Chenjin Jin1,
  14. Ping Zhang5,
  15. Yu Han6,
  16. Pisong Yan7,
  17. Ying Han8,
  18. Haotian Lin1,9
  1. 1 State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China
  2. 2 Shenzhen Eye Hospital, Shenzhen Key Laboratory of Ophthalmology, Affiliated Shenzhen Eye Hospital of Jinan University, Shenzhen, China
  3. 3 Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida, USA
  4. 4 Sylvester Comprehensive Cancer Centre, University of Miami Miller School of Medicine, Miami, Florida, USA
  5. 5 Xudong Ophthalmic Hospital, Inner Mongolia, China
  6. 6 EYE & ENT Hospital of Fudan University, Shanghai, China
  7. 7 Cloud Intelligent Care Technology (Guangzhou) Co. Ltd, Guangzhou, China
  8. 8 Department of Ophthalmology, University of California, San Francisco, California, USA
  9. 9 Center of Precision Medicine, Sun Yat-sen University, Guangzhou, China
  1. Correspondence to Haotian Lin, Stata Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou 510060, China; gddlht{at}


Background/Aims To develop a deep learning system for automated glaucomatous optic neuropathy (GON) detection using ultra-widefield fundus (UWF) images.

Methods We trained, validated and externally evaluated a deep learning system for GON detection based on 22 972 UWF images from 10 590 subjects that were collected at 4 different institutions in China and Japan. The InceptionResNetV2 neural network architecture was used to develop the system. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were used to assess the performance of detecting GON by the system. The data set from the Zhongshan Ophthalmic Center (ZOC) was selected to compare the performance of the system to that of ophthalmologists who mainly conducted UWF image analysis in clinics.

Results The system for GON detection achieved AUCs of 0.983–0.999 with sensitivities of 97.5–98.2% and specificities of 94.3–98.4% in four independent data sets. The most common reasons for false-negative results were confounding optic disc characteristics caused by high myopia or pathological myopia (n=39 (53%)). The leading cause for false-positive results was having other fundus lesions (n=401 (96%)). The performance of the system in the ZOC data set was comparable to that of an experienced ophthalmologist (p>0.05).

Conclusion Our deep learning system can accurately detect GON from UWF images in an automated fashion. It may be used as a screening tool to improve the accessibility of screening and promote the early diagnosis and management of glaucoma.

  • Diagnostic tests/Investigation
  • Glaucoma
  • Imaging

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  • ZL, CG, and DL contributed equally

  • Correction notice This article has been updated since it was published online. We have added the following information: ‘ZL, CG, and DL contributed equally’.

  • Contributors Conception and design: ZL, CG, DL and HL. Funding obtainment: HL. Provision of study data: HL, DN and PZ. Collection and assembly of data: ZL, DL, XZ, DW, MD, FX, PY, JW and PZ. Data analysis and interpretation: ZL, CG, DN, YZ, CC, XZ, DW, MD, FX, HL, DL, CJ, YH, PY, LZ and YH. Manuscript writing: all authors. Final approval of the manuscript: all authors.

  • Funding This study received funding from the National Key R&D Program of China (grant no. 2018YFC0116500), the National Natural Science Foundation of China (grant no. 81770967), the National Natural Science Fund for Distinguished Young Scholars (grant no. 81822010), the Science and Technology Planning Projects of Guangdong Province (grant no. 2018B010109008) and the Key Research Plan for the National Natural Science Foundation of China in Cultivation Project (grant no. 91846109). The sponsors and funding organisations had no role in the design or conduct of this research.

  • Competing interests None declared.

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

  • Data availability statement The data sets generated and/or analysed during the current study are available upon reasonable request from the corresponding author. Correspondence and requests for data materials should be addressed to HL (haot.lin{at}

  • 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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