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Artificial intelligence (AI) is the fourth industrial revolution.1 Deep learning is a robust machine learning technique that uses convolutional neural network to perform multilevel data abstraction without the need for manual feature engineering.2 In ophthalmology, many studies showed comparable, if not better, diagnostic performance in using AI to screen, diagnose, predict and monitor various eye conditions on fundus photographs and optical coherence tomography,3 4 including diabetic retinopathy (DR),5 age-related macular degeneration,6 glaucoma,7 retinopathy of prematurity (ROP).8
To date, many countries have reported well-established telemedicine programme to screen for DR and ROP,9–12 but limited for cataracts. Cataract is the leading cause of reversible blindness, affecting approximately 12.6 million (3.4–28.7 million) worldwide.13 14 The prevalence of cataract-related visual impairment also varies between high-income and low-income countries, with the latter having poorer access to tertiary care.13 In this issue, Wu et al 15 reported an AI-integrated telemedicine platform to screen and refer patients with cataract. This article consists of two parts: (1) the first part focusing on the AI system in detection of three tasks (capture mode, cataract diagnosis and referable cataract) and (2) the second part describing how these AI algorithms could be integrated in the telemedicine platform for real-world operational use. In this study, the referable cases were defined as: (1) grade 3 and grade 4 nuclear sclerotic …
Contributors DSJT and DST contributed to the drafting, DSJT, MA, JM and DST contributed to critical review and final approval for this editorial letter.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests DST is the co-inventor and patent holder of a deep learning system for retinal diseases.
Patient consent for publication Not required.
Provenance and peer review Commissioned; internally peer reviewed.