Vol. 10, Issue 11, Part C (2024)

The field of ophthalmology has seen tremendous transformation with the introduction of artificial intelligence (AI) in healthcare. The detection of retinal disorders is one of the most significant uses of AI in this sector. This study investigates the application of artificial intelligence (AI) associated with machine learning (ML) to the diagnosis of retinal diseases. Specifically, it focusses on the development and application of machine learning (ML) algorithms, particularly deep learning models, to the detection and classification of retinal diseases, including glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy. The study looks at how AI may help with early detection and individualised treatment plans, which can improve patient outcomes, reduce workload for healthcare workers, and improve diagnostic accuracy. Retinal diseases are the primary cause of blindness in developed nations; they account for the majority of visually impaired children, working-age adults (inherited retinal disease), and elderly people (age-related macular degeneration). These conditions require specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. As the world's population ages, this is becoming a global health priority. One way to address this issue is the development of artificial intelligence (AI) software to speed up data processing. Deep learning applied to automated segmentation has made it possible to accurately detect features like intraretinal fluid, subretinal fluid, drusen, pigment epithelial detachment, and geographic atrophy with an accuracy that is comparable to human graders. OCT Angiography (OCTA) uses sequentially acquired OCT-B scans to construct a depth-resolved image of retinal vasculature.

In this article, we explain the basic ideas of artificial intelligence (AI), such as machine learning (ML) and deep learning (DL) [2], and how they are applied in ophthalmology. We emphasise the importance of AI-driven treatments for the complexity and diversity of retinal disorders. In addition, we explore the particular uses of AI in the treatment of retinal diseases, including best vitelliform macular dystrophy, diabetic retinopathy (DR), age-related macular degeneration (AMD), macular neovascularisation, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, and sickle cell retinopathy. We concentrate on the state-of-the-art in artificial intelligence technologies, including different models, performance indicators, and therapeutic applications.

In summary, this paper advocates for a synergistic approach to healthcare delivery by highlighting AI's collaborative role with healthcare practitioners. It emphasises how crucial it is to use AI to supplement human knowledge rather than to replace it in order to fully realise its promise to transform healthcare delivery, reduce healthcare inequities, and enhance patient outcomes in the rapidly changing field of medicine.