Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

Abstract

Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI’s potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI’s vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.

Artificial Intelligence in Glaucoma: A New Landscape of Diagnosis and Management

“…For instance, deep learning algorithms may exhibit different accuracies when presented with different ethnic groups depending on fundus pigmentation and optic disc sizes [75]. Moreover, most datasets used for AI model training include images or data from European or Asian backgrounds, with lower African representation [76]. A lack of dataset diversity may cause the AI models to perform poorly when presented with newly acquired data from a different population in a realworld clinical setting, potentially leading to underdiagnosis or overdiagnosis of patients belonging to specific demographic backgrounds.…”

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

“…AI is revolutionizing the diagnosis and progression tracking of glaucoma, enhancing the efficiency and accuracy of disease detection and management [ 22 - 24 ]. Across various imaging modalities such as OCT, fundus photography, and visual field (VF) testing, AI algorithms have exhibited outstanding performance in detecting glaucoma [ 22 - 24 ]. In diagnosing glaucoma, AI strategies analyze retinal photographs and synthesize risk factors to identify high-risk patients requiring further evaluation [ 22 ].…”

“…Across various imaging modalities such as OCT, fundus photography, and visual field (VF) testing, AI algorithms have exhibited outstanding performance in detecting glaucoma [ 22 - 24 ]. In diagnosing glaucoma, AI strategies analyze retinal photographs and synthesize risk factors to identify high-risk patients requiring further evaluation [ 22 ]. Deep learning techniques interpret OCT, VF testing, and other ocular imaging results to detect characteristic glaucomatous patterns [ 22 - 24 ].…”

“…In diagnosing glaucoma, AI strategies analyze retinal photographs and synthesize risk factors to identify high-risk patients requiring further evaluation [ 22 ]. Deep learning techniques interpret OCT, VF testing, and other ocular imaging results to detect characteristic glaucomatous patterns [ 22 - 24 ]. Combining structural and functional inputs like OCT and VF data has significantly enhanced diagnostic accuracy, with area under receiver operating curve (AROC) values surpassing 0.90 [ 24 ].…”

“…Combining structural and functional inputs like OCT and VF data has significantly enhanced diagnostic accuracy, with area under receiver operating curve (AROC) values surpassing 0.90 [ 24 ]. Regarding glaucoma progression, AI algorithms have demonstrated the ability to detect progression earlier than conventional methods, potentially even from a single VF test [ 22 - 24 ]. AI-powered platforms facilitate continuous monitoring, with algorithms analyzing longitudinal data to alert physicians about rapid disease worsening promptly [ 22 , 23 ].…”