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Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning
  1. Zhiyuan Gao1,
  2. Xiangji Pan1,
  3. Ji Shao1,
  4. Xiaoyu Jiang2,
  5. Zhaoan Su1,
  6. Kai Jin1,
  7. Juan Ye1
  1. 1Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
  2. 2College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
  1. Correspondence to Professor Juan Ye, Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou 310009, Zhejiang, China; yejuan{at}zju.edu.cn; Dr Kai Jin; jinkai{at}zju.edu.cn

Abstract

Background/aims Fundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA images is complex and time-consuming, and the ability of diagnosis is uneven among different ophthalmologists. The aim of the study is to develop a clinically usable multilevel classification deep learning model for FFA images, including prediagnosis assessment and lesion classification.

Methods A total of 15 599 FFA images of 1558 eyes from 845 patients diagnosed with DR were collected and annotated. Three convolutional neural network (CNN) models were trained to generate the label of image quality, location, laterality of eye, phase and five lesions. Performance of the models was evaluated by accuracy, F-1 score, the area under the curve and human-machine comparison. The images with false positive and false negative results were analysed in detail.

Results Compared with LeNet-5 and VGG16, ResNet18 got the best result, achieving an accuracy of 80.79%–93.34% for prediagnosis assessment and an accuracy of 63.67%–88.88% for lesion detection. The human-machine comparison showed that the CNN had similar accuracy with junior ophthalmologists. The false positive and false negative analysis indicated a direction of improvement.

Conclusion This is the first study to do automated standardised labelling on FFA images. Our model is able to be applied in clinical practice, and will make great contributions to the development of intelligent diagnosis of FFA images.

  • Imaging
  • Telemedicine
  • Retina

Data availability statement

Data are available on reasonable request. Data are available by contacting the corresponding authors on reasonable request.

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Data availability statement

Data are available on reasonable request. Data are available by contacting the corresponding authors on reasonable request.

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Footnotes

  • Contributors ZG, KJ and JY conceived and designed the experiments. XP, JS and ZS collected and processed the data. ZG and XJ analysed the results. All authors reviewed the manuscript. JY is guarantor.

  • Funding This work was financially supported by National Natural Science Foundation of China (grant number U20A20386), National key research and development programme of China (grant number 2019YFC0118400), Key research and development programme of Zhejiang Province (grant number 2019C03020), Medical and Health Science and Technology Program of Zhejiang Province (grant number 2021RC064) and Clinical Medical Research Center for Eye Diseases of Zhejiang Province (grant number 2021E50007).

  • 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.