Automatic detection of diabetic retinopathy and its progression in sequential fundus images of patients with diabetes

Dietzel, Alexander; Schanner, Carolin; Falck, Aura; Hautala, Nina

doi:10.1111/aos.13976

Cited by 1 publication

(2 citation statements)

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“…4 The asymptomatic nature of retinopathy in the initial stages means that regular screening via digital imaging is promising for early detection and timely treatment. 5 Color fundus imaging is a non-invasive cost-effective tool for ophthalmological examinations. 6 A number of models based on convolutional neural networks (CNNs) have been developed to facilitate the classification of retinopathies based on color fundus images.…”

Section: Introductionmentioning

confidence: 99%

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PADAr: physician-oriented artificial intelligence-facilitating diagnosis aid for retinal diseases

et al. 2022

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Purpose: Retinopathy screening via digital imaging is promising for early detection and timely treatment, and tracking retinopathic abnormality over time can help to reveal the risk of disease progression. We developed an innovative physician-oriented artificial intelligence-facilitating diagnosis aid system for retinal diseases for screening multiple retinopathies and monitoring the regions of potential abnormality over time.Approach: Our dataset contains 4908 fundus images from 304 eyes with image-level annotations, including diabetic retinopathy, age-related macular degeneration, cellophane maculopathy, pathological myopia, and healthy control (HC). The screening model utilized a VGG-based feature extractor and multiple-binary convolutional neural network-based classifiers. Images in time series were aligned via affine transforms estimated through speeded-up robust features. Heatmaps of retinopathy were generated from the feature extractor using gradient-weighted class activation mapping++, and individual candidate retinopathy sites were identified from the heatmaps using clustering algorithm. Nested cross-validation with a train-to-test split of 80% to 20% was used to evaluate the performance of the screening model.Results: Our screening model achieved 99% accuracy, 93% sensitivity, and 97% specificity in discriminating between patients with retinopathy and HCs. For discriminating between types of retinopathy, our model achieved an averaged performance of 80% accuracy, 78% sensitivity, 94% specificity, 79% F1-score, and Cohen's kappa coefficient of 0.70. Moreover, visualization results were also shown to provide reasonable candidate sites of retinopathy.Conclusions: Our results demonstrated the capability of the proposed model for extracting diagnostic information of the abnormality and lesion locations, which allows clinicians to focus on patient-centered treatment and untangles the pathological plausibility hidden in deep learning models.

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Section: Introductionmentioning

confidence: 99%

“… 4 The asymptomatic nature of retinopathy in the initial stages means that regular screening via digital imaging is promising for early detection and timely treatment. 5 …”

Section: Introductionmentioning

confidence: 99%