Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases

• Published in: Clinical and experimental optometry Vol. 107; no. 2; pp. 130 - 146
• Main Authors: Hasan, Md Mahmudul, Phu, Jack, Sowmya, Arcot, Meijering, Erik, Kalloniatis, Michael
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis Ltd 01.03.2024
• Subjects: Artificial Intelligence; Deep learning; Diagnosis; Eye diseases; Glaucoma; Glaucoma - diagnosis; Humans; Learning algorithms; Literature reviews; Machine Learning; Medical diagnosis; Neurodegenerative diseases; Neurodegenerative Diseases - diagnostic imaging; Retina; Sensitivity and Specificity; deep learning; optical coherence tomography; glaucoma; dementia; Huntington’s disease; visual field; machine learning; Artificial intelligence; perimetry; Parkinson’s disease
• ISSN: 0816-4622; 1444-0938; 1444-0938
• DOI: 10.1080/08164622.2023.2235346

Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. Machine learning and deep learning - two primary data-driven pattern analysis approaches under the umbrella of artificial intelligence - has created considerable interest in the last few decades. The evolution of technology has resulted in a substantial amount of artificial intelligence research on ophthalmic and neurodegenerative disease diagnosis using retinal images. Various artificial intelligence-based techniques have been used for diagnostic purposes, including traditional machine learning, deep learning, and their combinations. Presented here is a review of the literature covering the last 10 years on this topic, discussing the use of artificial intelligence in analysing data from different modalities and their combinations for the diagnosis of glaucoma and neurodegenerative diseases. The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis. Generally, the accuracy of artificial intelligence-assisted diagnosis ranges from 67-98%, and the area under the sensitivity-specificity curve (AUC) ranges from 0.71-0.98, which outperforms typical human performance of 71.5% accuracy and 0.86 area under the curve. This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis. The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice.