The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma

Abstract

Glaucomatous optic neuropathy is the leading cause of irreversible blindness worldwide. Diagnosis and monitoring of disease involves integrating information from the clinical examination with subjective data from visual field testing and objective biometric data that includes pachymetry, corneal hysteresis, and optic nerve and retinal imaging. This intricate process is further complicated by the lack of clear definitions for the presence and progression of glaucomatous optic neuropathy, which makes it vulnerable to clinician interpretation error. Artificial intelligence (AI) and AIenabled workflows have been proposed as a plausible solution. Applications derived from this field of computer science can improve the quality and robustness of insights obtained from clinical data that can enhance the clinician's approach to patient care. This review clarifies key terms and concepts used in AI literature, discusses the current advances of AI in glaucoma, elucidates the clinical advantages and challenges to implementing this technology, and highlights potential future applications.

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

“…In response to these challenges, AI-enabled screening for glaucoma could help fill this unmet need, increasing access to care and lessening the burden on healthcare systems [19]. A system that accurately flags possible glaucoma on images in real time could allow for large-scale screenings to be conducted without the presence of a vision specialist.…”

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“…This intricate process is further complicated by the lack of clear definitions for the presence and progression of glaucomatous optic neuropathy, which makes it vulnerable to clinician interpretation error. AI and AIenabled workflows are plausible solutions [18] . AI has many applications for the anterior segment of the eye, which can improve diagnosis, treatment, and outcomes for various ocular conditions [19][20] .…”

Artificial intelligence and glaucoma progression

“…One of the barriers to this adoption is the complexity of the AI algorithms, which is not a familiar language for most of the clinicians. 32 The incorporation of these new AI developments requires the interaction with manufactures to create in-built software that can be user-friendly for the clinician. 33 Currently, we have a huge amount of medical data available in clinics and hospitals that can be shared with increased velocity through internet cloud systems.…”

“…34 However, to do so, collaborative networking for data collection, infrastructure capacity to storage, trained specialists to process and analyze the data and a broader discussion over regulatory laws and cybersecurity are needed. 32,35…”