ArticleScientific reports2024
A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A review of optimization strategies for deep and machine learning in diabetic macular edema.Frontiers in artificial intelligence · 2026Pooled it
- Quantitative Analysis of Retinal Fluid by a Deep Learning Model in Uveitic Macular Edema.Ophthalmology science · 2026Article
- Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.Translational vision science & technology · 2026Article
- Fine Tuning RETFound with Clinically Guided Foveal ROI for Automated DRIL Classification in Diabetic Macular Edema OCT.Diagnostics (Basel, Switzerland) · 2026Article
- Automated macular hole staging on optical coherence tomography using an optimized ResNet-18 framework.Frontiers in cell and developmental biology · 2026Article
- A lightweight DeepME model based on improved YOLOv11 architecture for macular edema detection and treatment monitoring.Frontiers in endocrinology · 2026Article
- Self-Supervised Representation Learning for Data-Efficient DRIL Classification in OCT Images.Diagnostics (Basel, Switzerland) · 2025Article
- FISM: harnessing deep learning and reinforcement learning for precision detection of microaneurysms and retinal exudates for early diabetic retinopathy diagnosis.BioData mining · 2025Article
- Deep learning based retinal hard exudates quantification of optical coherence tomography.International journal of retina and vitreous · 2025Article
- Artificial intelligence derived grading of mustard gas induced corneal injury and opacity.Scientific reports · 2025Article
- AI Quantification of Vascular Lesions in Mouse Fundus Fluorescein Angiography.Translational vision science & technology · 2025Article
- Hyperreflective Retinal Foci (HRF): Definition and Role of an Invaluable OCT Sign.Journal of clinical medicine · 2025Article
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9 authors.
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Abstract
The purpose of the study was to detect Hard Exudates (HE) and classify Disorganization of Retinal Inner Layers (DRIL) implementing a Deep Learning (DL) system on optical coherence tomography (OCT) images of eyes with diabetic macular edema (DME). We collected a dataset composed of 442 OCT images on which we annotated 6847 HE and the presence of DRIL. A complex operational pipeline was defined to implement data cleaning and image transformations, and train two DL models. The state-of-the-art neural network architectures (Yolov7, ConvNeXt, RegNetX) and advanced techniques were exploited to aggregate the results (Ensemble learning, Edge detection) and obtain a final model. The DL approach reached good performance in detecting HE and classifying DRIL. Regarding HE detection the model got an AP@0.5 score equal to 34.4% with Precision of 48.7% and Recall of 43.1%; while for DRIL classification an Accuracy of 91.1% with Sensitivity and Specificity both of 91.1% and AUC and AUPR values equal to 91% were obtained. The P-value was lower than 0.05 and the Kappa coefficient was 0.82. The DL models proved to be able to identify HE and DRIL in eyes with DME with a very good accuracy and all the metrics calculated confirmed the system performance. Our DL approach demonstrated to be a good candidate as a supporting tool for ophthalmologists in OCT images analysis.
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