Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care

Introduction

Glaucoma is the leading cause of irrevasible blindness and a significant public health concern globally^{1, 2–3} with its prevalence increasing steadily due to aging populations and other risk factors⁴. Despite its prevalence and potentially devastating consequences if left untreated, glaucoma often goes undiagnosed, particularly in its early stages. Globally, over 70% of people with glaucoma remain undiagnosed⁵. This underlines the critical need for effective and accessible screening methods to detect glaucoma early and prevent irreversible vision loss. The impact of undiagnosed glaucoma is not only limited to individual patients but also extends to the healthcare system and society as a whole, highlighting the need for implementation of comprehensive screening strategies.

The importance of glaucoma screening is further underscored by the challenges associated with its detection. Traditional screening methods, while effective to some extent, often face limitations in terms of accessibility, cost, and resource requirements⁶. For example, in Australia, as well as in many other Western countries such as the USA, UK, and Canada, there are theoretically two primary care models for patients with eye problems– involving General Practitioners (GPs) and optometrists. However, in practice, GPs typically lack both the equipment and training necessary to diagnose glaucoma⁷. This gap can lead to two critical issues: missed diagnoses (effectively 0% sensitivity) or inaccurate referrals, which may overwhelm specialist services with false positives (low specificity). Both outcomes delay care for those who truly need it. In Australia, for instance, the median wait time for a public hospital ophthalmology appointment is currently 400 days⁸.

Furthermore, glaucoma diagnosis is like an American breakfast—composed of multiple essential tests that together form a complete picture. A key component is evaluating the optic disc and retinal nerve fibre layer (RNFL). Characteristic changes, such as increased cup-to-disc ratio (CDR), rim notching, and RNFL thinning, can appear before visual field defects, making fundus image interpretation crucial. However, detecting these signs can be challenging, especially in primary care settings where advanced imaging tools like OCT may not be available.

Artificial intelligence (AI) has emerged as a promising tool in the field of ophthalmology, offering the potential to revolutionize glaucoma detection^{9, 10, 11, 12, 13, 14, 15, 16–17}. AI algorithms can analyse large...