Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification

Andersen, Jørn; Hubel, Martin Slusarczyk; Rasmussen, Malin Lundberg; Grauslund, Jakob; Savarimuthu, Thiusius Rajeeth

doi:10.1167/tvst.11.6.19

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…As part of a DR screening system, DeepDR, this architecture achieved AUCs of 0.901–0.967 for lesions detection, including microaneurysms, hard exudates, cotton-wool spots and hemorrhage, and the overall DR grading achieved an average AUC of 0.955 [ 17 ]. Anderson et al achieved improved performance by incorporating a segmentation model into a DR classification framework, with manually segmenting 34,075 DR lesions to develop a segmentation model, and then constructing a 5-step classification model [ 18 ]. Together with DR screening systems, these AI models can collectively deliver a more detailed evaluation, with screening systems acting as a broad initial assessment and lesion segmentation models providing more in-depth analysis of disease status.…”

Section: Main Textmentioning

confidence: 99%

Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema

Yao,

Lim,

Lim

et al. 2024

Eye and Vis

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Background Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges. Main text This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications. Conclusion AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.

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Section: Main Textmentioning

confidence: 99%