Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases

Abstract

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the “black box phenomenon”, biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.

Unveiling the Potential: A Comprehensive Review of Artificial Intelligence Applications in Ophthalmology and Future Prospects

“…The incorporation of AI into automated retinal image analysis provides a valuable tool for expanding screening initiatives, enhancing diagnostic precision, and facilitating early intervention for a wide spectrum of retinal diseases. These AI-driven systems not only streamline disease detection processes but also have the potential to transform the diagnosis and management of eye diseases, ultimately leading to better patient outcomes and improved access to timely care [ 9 ].…”

Genetic and Clinical Analyses of the KIZ-c.226C>T Variant Resulting in a Dual Mutational Mechanism

“…Population-based analysis indicated that 1 in 4000 individuals are affected by RP worldwide [ 5 ]. RP is characterized by significant variability among patients [ 6 , 7 , 8 ], with many exhibiting the classic symptoms such as impaired dark adaptation and night blindness during adolescence and loss of mid-peripheral visual field during young adulthood, both attributed to rod dysfunction [ 9 ]. As the disease progresses, patients suffer the loss of far peripheral vision, eventually developing tunnel vision, and ultimately experiencing the compromise of the central vision due to cone impairment in the later stages of life [ 5 ].…”

Whole-eye transplantation: Current challenges and future perspectives

“…Beyond traditional prosthetics, cutting-edge research into electronic visual prostheses, often referred to as "bionic eyes", aims to restore sight through the direct stimulation of visual pathways in the brain or the remaining parts of the visual system[ 10 ]. These devices, which can include retinal implants and cortical visual prostheses, translate external visual information captured by cameras into electrical signals that the brain can interpret, offering a form of sight to those with certain types of blindness[ 11 , 12 ].…”