Dataset Information

Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.

ABSTRACT:

Background

To verify efficacy of automatic screening and classification of glaucoma with deep learning system.

Methods

A cross-sectional, retrospective study in a tertiary referral hospital. Patients with healthy optic disc, high-tension, or normal-tension glaucoma were enrolled. Complicated non-glaucomatous optic neuropathy was excluded. Colour and red-free fundus images were collected for development of DLS and comparison of their efficacy. The convolutional neural network with the pre-trained EfficientNet-b0 model was selected for machine learning. Glaucoma screening (Binary) and ternary classification with or without additional demographics (age, gender, high myopia) were evaluated, followed by creating confusion matrix and heatmaps. Area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score were viewed as main outcome measures.

Results

Two hundred and twenty-two cases (421 eyes) were enrolled, with 1851 images in total (1207 normal and 644 glaucomatous disc). Train set and test set were comprised of 1539 and 312 images, respectively. If demographics were not provided, AUC, accuracy, precision, sensitivity, F1 score, and specificity of our deep learning system in eye-based glaucoma screening were 0.98, 0.91, 0.86, 0.86, 0.86, and 0.94 in test set. Same outcome measures in eye-based ternary classification without demographic data were 0.94, 0.87, 0.87, 0.87, 0.87, and 0.94 in our test set, respectively. Adding demographics has no significant impact on efficacy, but establishing a linkage between eyes and images is helpful for a better performance. Confusion matrix and heatmaps suggested that retinal lesions and quality of photographs could affect classification. Colour fundus images play a major role in glaucoma classification, compared to red-free fundus images.

Conclusions

Promising results with high AUC and specificity were shown in distinguishing normal optic nerve from glaucomatous fundus images and doing further classification.

SUBMITTER: Hung KH

PROVIDER: S-EPMC9743575 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.

Hung Kuo-Hsuan KH Kao Yu-Ching YC Tang Yu-Hsuan YH Chen Yi-Ting YT Wang Chuen-Heng CH Wang Yu-Chen YC Lee Oscar Kuang-Sheng OK

BMC ophthalmology 20221212 1

<h4>Background</h4>To verify efficacy of automatic screening and classification of glaucoma with deep learning system.<h4>Methods</h4>A cross-sectional, retrospective study in a tertiary referral hospital. Patients with healthy optic disc, high-tension, or normal-tension glaucoma were enrolled. Complicated non-glaucomatous optic neuropathy was excluded. Colour and red-free fundus images were collected for development of DLS and comparison of their efficacy. The convolutional neural network with ...[more]

PMID: 36510171

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Dataset Information

Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.

Background

Methods

Results

Conclusions

Publications

Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.

Similar Datasets

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