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Monitoring and Early Detection of Treatable Inherited RETinal Disease in Children and Adolescents

Who can join

Up to age 50 · All sexes · Healthy volunteers welcome

Inclusion Criteria:

Participants must meet the criteria for one of the following study cohorts:

* Retinal Pigment Epithelium Specific Protein 65 (RPE65)-associated retinal degeneration: genetically confirmed biallelic sequence variants in RPE65.
* Participants with a clinical diagnosis of Leber congenital amaurosis or early onset severe retinal degeneration whose phenotype is suggestive of biallelic RPE65-associated retinal degeneration will also be eligible for inclusion.
* Retinitis Pigmentosa GTPase Regulator (RPGR)-associated retinal degeneration: genetically confirmed hemizygous RPGR sequence variants in male participants, or female participants with single heterozygous RPGR sequence variants or homozygous or compound heterozygous RPGR sequence variants will be included in this cohort.
* Participants with a clinical diagnosis of X-linked Retinitis Pigmentosa and X-linked cone-rod degeneration whose phenotype is suggestive of RPGR-associated retinal degeneration will also be eligible for inclusion.
* Control cohorts: participants without inherited retinal degeneration, including both myopic (defined as mean spherical equivalent refractive error of ≤ -1.00 dioptres (D)) and non-myopic individuals. All will be from diverse ethnic backgrounds, for development of a normative retinal imaging dataset.
* Availability of retinal imaging data of sufficient quality for the planned analyses.

Exclusion Criteria

* Participants who do not meet the clinical, genetic, or control-group criteria for any of the predefined study cohorts.
* Participants for whom the retinal imaging data required for the planned analyses are unavailable.
* Participants whose retinal images are of insufficient quality for reliable image analysis.

About the study

The goals of this observational study are to develop and train artificial intelligence (AI) algorithms using multimodal imaging to identify patients with inherited retinal degeneration (IRD) caused by Retinal Pigment Epithelium Specific Protein 65 (RPE65) and Retinitis Pigmentosa GTPase Regulator (RPGR) mutations. Another algorithm will be developed to measure and monitor retinal changes over time, with the aim of helping identify patients who may be suitable for available or emerging treatments and supporting assessment of disease progression and treatment response.

The main questions this study aims to answer are:

1. Can AI algorithms accurately distinguish patients with RPE65- or RPGR-associated IRDs from individuals without these conditions using ultra-widefield retinal imaging? 2. Can AI algorithms accurately detect and quantify retinal abnormalities associated with these conditions and monitor their progression over time on ultra-widefield retinal imaging?

Researchers will compare retinal images from patients with RPE65- or RPGR-associated IRDs with images from healthy participants, including people with and without myopia, to assess how accurately the algorithms can distinguish between these groups and monitor disease-related changes.

Sponsor: University College, London · Participants: 4,887 · Started: Jan 1, 2026

Contact the study team

• Nikolas Pontikos, PhD, MSci, MEng, FBCS · Phone: +44 207608 6800

Official record on ClinicalTrials.gov — NCT07901907

Locations in the U.S.

From ClinicalTrials.gov, data retrieved Nov 15, 2026. Each study sets its own eligibility; the study team decides who can join.