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Abstract

Background
Community-based optometrists, a major provider of primary eye care in the United Kingdom, are the main source of referrals to hospital eye services. The widespread introduction of optical coherence tomography devices in community practices provides community-based optometrists with an opportunity to identify a broader range of treatable diseases. Standard referral pathways do not effectively filter unnecessary referrals, with misclassification of urgency, and erroneous diagnoses.

Objectives
To assess the effectiveness of a teleophthalmology referral pathway between community-based optometrists and hospital eye services for retinal diseases. To measure the accuracy of an artificial intelligence decision support system for diagnosis and referral management of retinal disease.

Design
A multicentre, superiority cluster randomised controlled trial to assess the effectiveness of a teleophthalmology referral pathway. A prospective, observational diagnostic accuracy study to measure the performance of artificial intelligence decision support system. A comprehensive economic evaluation was conducted.

Settings
United Kingdom-based community optometry practices with an optical coherence tomography device and hospital eye services.

Participants
Adults requiring referral for retinal disease at the opinion of the community-based optometrists.

Interventions
Community optometry practices were randomised 1 : 1 to standard care or teleophthalmology. Referrals sent via the teleophthalmology platform were remotely reviewed by human experts based at the corresponding hospital eye services. A referral decision was provided within 48 hours. Suitable optical coherence tomography scans were solely processed by artificial intelligence decision support system (the ‘Octane’ model).

Main outcome measures
Cluster randomised controlled trial’s primary outcome was the proportion of false-positive referrals (not required or not urgent) per arm in overall participants and in referred-only participants against an independent reference standard. Secondary outcomes included the proportion of wrong diagnosis, wrong referral urgency, false-negative referrals, safely triaged referrals for rare diseases, time from referral to consultation and treatment and cost-effectiveness of teleophthalmology. Primary outcome for the artificial intelligence study was the sensitivity and specificity of artificial intelligence referral decisions against the reference standard.

Results
Teleophthalmology significantly reduces the proportion of false-positive urgent referrals by 59% compared to standard care in referred participants. Due to the observed low event rate for false positive referrals, teleophthalmology’s role for reducing false positives overall was inconclusive. No significant difference between arms for safety of referral decisions (false negatives) was found. After accounting for external factors, the time to consultation demonstrated both clinically and statistically significant benefits for the teleophthalmology arm. The time to treatment showed a clinically significant benefit.

Of 396 recruited participants, the Octane artificial intelligence model processed images contributed by 204 participants (51.5%). For referral decisions, the model showed comparable sensitivity and specificity against its own preset referral rules (rule-based reference standard) (post hoc analysis), but it showed inferior sensitivity and specificity when compared to human expert assessors making these referral decisions (clinical reference standard) (primary AI analysis). The artificial intelligence model presented challenges relating to its generalisability in a real-world evaluation context.

Limitations
Technical limitations in optometry practices, lack of ethnicity data.

Conclusions
Asynchronous teleophthalmology reduces the number of unnecessary urgent referrals, the main drivers of increasing hospital capacity pressures, provides more appropriate referral-to-treatment times and is more cost-effective compared to standard care. The Octane artificial intelligence model could not process images from 48.5% of study participants. Compared to hospital-based experts for referral decisions, Octane was less accurate at making routine and urgent referral decisions and of similar accuracy to community optometrists.

Future work
Applied health research, human–artificial intelligence interaction and artificial intelligence clinical trial design.

Publication Type:	Article
Additional Information:	Copyright © 2025 Sharma et al. This work was produced by Sharma et al. under the terms of a commissioning contract issued by the Secretary of State for Health and Social Care. This is an Open Access publication distributed under the terms of the Creative Commons Attribution CC BY 4.0 licence, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. See: https://creativecommons.org/licenses/by/4.0/. For attribution the title, original author(s), the publication source – NIHR Journals Library, and the DOI of the publication must be cited.
Subjects:	R Medicine > RE Ophthalmology
Departments:	School of Health & Medical Sciences School of Health & Medical Sciences > Department of Optometry & Visual Science
SWORD Depositor:	Symplectic Administrator

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