一种基于 OCT 图像的深度学习 I 和 II 型 wAMD辅助分型方法

doi:10.3760/cma.j.cn115909-20210204-00046

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Abstract Objective: To investigate the application of a deep learning algorithm based on optical coherence tomography (OCT) images for the classification of wet age-related macular degeneration (wAMD) so as to assist in the diagnosis of ophthalmic diseases. Methods: Data of this study were collected from 39 patients (46 eyes) diagnosed with wAMD in the outpatient department of Ningbo Eye Hospital from June 2018 to June 2019. First, senior ophthalmologists provide the wAMD grade of each patient as the gold standard of the classification algorithm. Then, Resnet 34 was used to output the predicted results of the type of wAMD in order to compare these results with the gold standard. The parameters were fine-tuned continuously until the status of the loss was in convergence. Finally, the wAMD grade for patients was diagnosed automatically. Results: The accuracy of the algorithm is the proportion of the cases correctly predicted in all test cases. The experimental results showed that the accuracy rate of identifying the type of wAMD based on the deep learning network was 20% higher than for general ophthalmologists. Gradientweighted class activation mapping can visualize how the model can be used as a diagnostic reference for ophthalmologists. Conclusion: In the aspects of classifying wAMD by an adequately trained deep learning algorithm, its accuracy is significantly better than that of general ophthalmologists. Therefore, the classification results based on deep learning algorithms can be used for auxiliary diagnosis of diseases, so as to alleviate the scarcity of domestic ophthalmologists.

Key words： deep learning wet age-related macular degeneration lesion classification optical coherence tomography

Received: 04 February 2021

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Fund: Ningbo Natural Science Foundation Project (2019A610354); Zhejiang Natural Science Foundation (LQ19H180001); Zhejiang General Project of Natural Science Foundation (LY19H120001); Ningbo Public Welfare Plan General Project (2019C50049); 2021 Zhejiang Traditional Chinese Medicine Science and Technology Plan (2021ZB268)

Corresponding Authors: Yan Hu, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China (Email: huy3@sustech.edu.cn)

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https://www.cjoovs.com/EN/10.3760/cma.j.cn115909-20210204-00046 OR https://www.cjoovs.com/EN/Y2021/V23/I8/615

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