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22.02.2021 | Retinal Disorders

Classification of pachychoroid on optical coherence tomography using deep learning

Graefe's Archive for Clinical and Experimental Ophthalmology
Nam Yeo Kang, Ho Ra, Kook Lee, Jun Hyuk Lee, Won Ki Lee, Jiwon Baek
Wichtige Hinweise
Nam Yeo Kang and Ho Ra contributed equally to this work.
This article is part of a topical collection on Breakthroughs in artificial intelligence for ophthalmology.

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Pachychoroid is characterized by dilated Haller vessels and choriocapillaris attenuation that are seen on optical coherence tomography (OCT) B-scans. This study investigated the feasibility of using deep learning (DL) models to classify pachychoroid and non-pachychoroid eyes from OCT B-scan images.


In total, 1898 OCT B-scan images were collected from eyes with macular diseases. Images were labeled as pachychoroid or non-pachychoroid based on strict quantitative and qualitative criteria for multimodal imaging analysis by two retina specialists. DL models were trained (80%) and validated (20%) using pretrained convolutional neural networks (CNNs). Model performance was assessed using an independent test set of 50 non-pachychoroid and 50 pachychoroid images.


The final accuracy of AlexNet and VGG-16 was 57.52% for both models. ResNet50, Inception-v3, Inception-ResNet-v2, and Xception showed a final accuracy of 96.31%, 95.25%, 93.40%, and 92.61%, respectively, for the validation set. These models demonstrated accuracy on an independent test set of 78.00%, 86.00%, 90.00%, and 92.00%, and an F1 score of 0.718, 0.841, 0.894, and 0.920, respectively.


DL models classified pachychoroid and non-pachychoroid images with good performance. Accurate classification can be achieved using CNN models with deep rather than shallow neural networks.

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