The application of artificial intelligence in glaucoma diagnosis and prediction

Abstract

Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.

The utilization of artificial intelligence in glaucoma: diagnosis versus screening

“…When deep learning machines are trained by a large VF dataset and their models are processed to make VF features more susceptible to detection, AI algorithms can predict, diagnose and monitor glaucoma with high accuracy gains, low cost and raised efficiency (3) 37). Furthermore, a smartphone application called "iGlaucoma" DL system was developed to interpret VF pattern deviation and was found to be a clinically effective tool to detect glaucomatous optic neuropathy showing its promise for clinical applicability (38).…”

Transforming Glaucoma Care with Artificial Intelligence: A Comprehensive Review of Diagnosis, Monitoring, and Treatment Algorithms

“…Implementing a robust AI system that enhances objectivity could lead to more precise glaucoma diagnoses. In recent developments, progress in AI has been directed toward accurately identifying and measuring optic disc and cup characteristics in fundus photographs to determine CDR values [8].…”

Utility of artificial intelligence‐based large language models in ophthalmic care

“…Machine learning requires ‘supervised learning’ where experts label and grade individual features and severity from images to develop the AI. A subset of machine learning is deep learning that shows promise in disease screening, diagnosis, risk stratification, treatment monitoring and improved patient care for eyes with myopia, 4 optic disc abnormalities (e.g., glaucoma, papilledema), 5–7 retinal diseases (e.g., age‐related macular degeneration, diabetic retinopathy), 2 cataract 2 and corneal disorders 8,9 . Deep learning, referred to as ‘unsupervised learning’, bypasses this need to label or grade individual features, and instead uses features of the entire image to compare with a diagnosis determined by an expert 10 .…”