DDLSNet: A Novel Deep Learning-Based System for Grading Funduscopic Images for Glaucomatous Damage

Rasheed, Haroon Adam; Davis, Tyler A.; Morales, Esteban; Fei, Zhe; Grassi, Lourdes; Gainza, Agustina De; Nouri‐Mahdavi, Kouros

doi:10.1016/j.xops.2022.100255

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…The component CNNs were selected based on performance in a variety of prior ophthalmic applications 13 – 19 . Since the data set of 678 images contained only 120 images (18%) with grader-identified neovascular leakage, additional weight was placed on this classification to address the class imbalance.…”

Section: Resultsmentioning

confidence: 99%

Deep learning for automated detection of neovascular leakage on ultra-widefield fluorescein angiography in diabetic retinopathy

Zhao

Bommakanti

et al. 2023

Sci Rep

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Diabetic retinopathy is a leading cause of blindness in working-age adults worldwide. Neovascular leakage on fluorescein angiography indicates progression to the proliferative stage of diabetic retinopathy, which is an important distinction that requires timely ophthalmic intervention with laser or intravitreal injection treatment to reduce the risk of severe, permanent vision loss. In this study, we developed a deep learning algorithm to detect neovascular leakage on ultra-widefield fluorescein angiography images obtained from patients with diabetic retinopathy. The algorithm, an ensemble of three convolutional neural networks, was able to accurately classify neovascular leakage and distinguish this disease marker from other angiographic disease features. With additional real-world validation and testing, our algorithm could facilitate identification of neovascular leakage in the clinical setting, allowing timely intervention to reduce the burden of blinding diabetic eye disease.

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Section: Resultsmentioning

confidence: 99%