Application of artificial intelligence in cataract management: current and future directions

Area of Science:

• Ophthalmology
• Medical Artificial Intelligence (AI)
• Medical Imaging Analysis

Background:

• Cataract is a leading cause of reversible visual impairment globally, with a growing clinical burden.
• Limited access to specialized ophthalmic care, particularly in developing nations, exacerbates the challenge.
• The COVID-19 pandemic has further highlighted the need for remote and efficient healthcare solutions.

Purpose of the Study:

• To explore the transformative potential of artificial intelligence (AI) in enhancing cataract management.
• To identify key AI applications that can improve diagnosis, monitoring, surgical training, and workflow efficiency.
• To address geographical barriers and improve access to ophthalmic services through AI-driven solutions.

Main Methods:

• Utilizing deep-learning, convolutional neural networks (CNNs) for telediagnostic screening and classification of cataracts from medical images.
• Employing AI algorithms to improve the accuracy of intraocular lens (IOL) power calculation formulas.
• Leveraging AI for surgical skill training by analyzing video footage of cataract surgeries and predicting procedure durations.

Main Results:

• AI demonstrates robust capabilities in screening and diagnosing diabetic retinopathy, macular degeneration, glaucoma, and retinopathy of prematurity.
• AI-enhanced IOL formulas achieve superior postoperative refractive prediction compared to traditional methods.
• AI models can predict posterior capsule opacification progression and the need for YAG laser capsulotomy.

Conclusions:

• AI has the potential to revolutionize cataract management by increasing automation, efficacy, and accessibility.
• AI applications can overcome geographical limitations, improving ophthalmic service delivery, especially in underserved regions.
• Key challenges include data ethics, security, model generalizability, clinical validation, and user trust.