AI IN THE DIAGNOSIS AND TREATMENT OF GLAUCOMA
Abstract
As the leading cause of permanent vision loss worldwide, glaucoma poses a significant public health problem because it is a condition that progresses slowly and has highly complex multi-modal diagnostic needs. This paper offers a broad review of how Artificial Intelligence (AI) has developed through the clinical spectrum of glaucoma treatment, ranging from initial diagnosis and glaucoma severity, to now personalized treatment planning and surgical guidance. The AI models use deep learning architectures, such as Convolutional Neural Networks (CNNs) and multi-modal transformers, to automatically process intricate diagnostic data like color fundus photography, Optical Coherence Tomography (OCT) scans, visual field perimetry, and Electronic Health Records (EHRs). In addition, predictive algorithms can enable personalized treatment based on the prediction of disease progression rates, intraocular pressure fluctuations and the selection of more appropriate surgical candidates. Although the advantages are clear, there are important obstacles to broader adoption in clinical practice, such as variations in hardware across different devices, risk of bias in the algorithms used by different patient populations, data privacy issues, and the 'black-box' nature of deep learning. Newly developed concepts such as the Explainable AI, the use of wearable sensors for continuous monitoring, and the use of genomic risk profiles are bridging these gaps. In the end, AI is an objective, rapid, clinical decision support tool to assist clinical practice, improve the efficiency of teleophthalmology screenings and protect the patient's visual function.
