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Erschienen in: Graefe's Archive for Clinical and Experimental Ophthalmology 7/2021

22.02.2021 | Retinal Disorders

Classification of pachychoroid on optical coherence tomography using deep learning

verfasst von: Nam Yeo Kang, Ho Ra, Kook Lee, Jun Hyuk Lee, Won Ki Lee, Jiwon Baek

Erschienen in: Graefe's Archive for Clinical and Experimental Ophthalmology | Ausgabe 7/2021

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Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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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Metadaten
Titel
Classification of pachychoroid on optical coherence tomography using deep learning
verfasst von
Nam Yeo Kang
Ho Ra
Kook Lee
Jun Hyuk Lee
Won Ki Lee
Jiwon Baek
Publikationsdatum
22.02.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Graefe's Archive for Clinical and Experimental Ophthalmology / Ausgabe 7/2021
Print ISSN: 0721-832X
Elektronische ISSN: 1435-702X
DOI
https://doi.org/10.1007/s00417-021-05104-4

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