An observational study in Inherited Retinal Degeneration Associated With RPGR Mutations and Inherited Retinal Degeneration Associated With Biallelic RPE65 Mutations, sponsored by University College, London. Recruiting at 3 sites in 3 countries. Open to participants aged Up to 50 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-10-28.
Sponsored by University College, London · Observational
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:
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.
Our study is centred on tackling inherited retinal degeneration (IRD), which is a significant cause of vision loss in children and adolescents around the world. Despite their substantial burden of disease, treatment options remain limited. To date, voretigene neparvovec (VN) remains the only approved gene therapy for IRDs, specifically RPE65-associated mutations, although novel therapies targeting RPGR-associated IRDs are emerging. Modern non-invasive retinal imaging techniques, in particular ultra widefield (UWF) imaging for both color and fundus autofluorescence (FAF) acquired using Optos, Zeiss-Clarus and Heidelberg Engineering retinal scanners, are now increasingly adopted in the detection and monitoring of IRDs, for natural history, clinical trials and postmarket surveillance. This offers new opportunities for definition and detection of novel biomarkers. UWF retinal imaging is especially well suited for IRDs, since for some the earliest changes start from the periphery. Also, IRD patients often have fixation instability or nystagmus which complicate volume scan acquisition with Optical Coherence Tomography which is not a problem with UWF retinal imaging. Functional testing of the periphery of the retina is equally challenging to obtain due to reduced cooperation in children, so reliable visual field data can only be obtained from children who are 8 years old and older. By then, the optimal treatment window may already have passed. UWF retinal imaging requires only a very short fixation time, so high quality images can be obtained easily, independent of age and fixation capacity enabling longitudinal monitoring. However, analysis and interpretation of UWF retinal images remain very manual and qualitative for IRDs, despite the increasing amount of data available. Detection of IRDs from imaging is also crucial because even with modern genetic testing, a mutation cannot always be detected, so an IRD cannot be ruled out on the basis of negative genetic testing. Another problem of mutation analysis are variants of uncertain significance which AI may help reclassify as pathogenic or non-pathogenic. To this purpose we will develop a less invasive approach that harnesses the power of AI that can identify the genetic causes of IRDs from retinal images. Accurate longitudinal monitoring of IRDs is also of critical importance to carefully monitor any adverse effects post-treatment compared to natural progression, especially as treatment options are expanding.
Ultimately, the study seeks to advance AI-assisted phenotypic characterisation of IRDs and establish quantitative imaging approaches that could support earlier diagnosis, improve longitudinal disease monitoring and facilitate the evaluation of emerging therapies in future clinical trials.
132 studies on the registry are indexed under Retinal Degeneration; 23 are open to participants now.
This study's planned enrollment of 4,887 is above the median of 78 across 32 observational studies indexed under Retinal Degeneration.
Browse Retinal Degeneration studies →University College, London is the lead sponsor of 651 studies on the registry; 149 are open to participants now.
Of its 6 completed or terminated interventional studies of FDA-regulated products, 2 (33%) have results posted.
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The study population will consist of patients with inherited retinal degeneration (IRD) and non-IRD control participants identified through participating tertiary ophthalmology and inherited retinal degeneration centers across international study sites. Clinical, demographic and imaging data will be obtained from existing institutional records and imaging databases. Non-IRD myopic and non-myopic control participants will be selected from the same or comparable populations to provide an appropriate reference dataset.
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Participants must meet the criteria for one of the following study cohorts:
Exclusion Criteria
Participants with genetically confirmed biallelic pathogenic or likely pathogenic sequence variants in RPE65 will be included in this cohort. Some of the participants may have received therapy in the form of gene augmentation with voretigene neparvovec. 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.
Participants with 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.
Participants without myopia and without evidence of inherited retinal degeneration, will be included as one of the two control cohorts.
Participants with myopia, defined as mean spherical equivalent refractive error of ≤ -1.00 dioptres and without evidence of inherited retinal degeneration will be included.
Performance of an Artificial Intelligence (AI) algorithm for the diagnosis of Inherited Retinal Degeneration Associated With Biallelic RPE65- mutations using Ultra-Wide field retinal imaging
The primary outcome will be the performance of the artificial intelligence algorithm for the diagnosis of RPE65- associated retinal degeneration using wide-field color/ pseudocolor fundus images acquired with Optos and Clarus and fundus autofluorescence images acquired from Clarus and Heidelberg engineering imaging systems. Performance will be evaluated on an independent held-out test dataset using standard classification metrics, including sensitivity (recall), specificity, precision (positive predictive value), negative predictive value, accuracy, F1 score, and area under the receiver operating characteristic curve (AUROC).
Time frame: Up to approximately 1 year after study completion.
Performance of an Artificial Intelligence (AI) algorithm for the diagnosis of Inherited Retinal Degeneration Associated With RPGR- mutations using Ultra-Wide field retinal imaging
The primary outcome will be the performance of the artificial intelligence algorithm for the diagnosis of RPGR- associated retinal degeneration using wide-field color/ pseudocolor fundus images acquired with Optos and Clarus and fundus autofluorescence images acquired from Clarus and Heidelberg engineering imaging systems. Performance will be evaluated on an independent held-out test dataset using standard classification metrics, including sensitivity (recall), specificity, precision (positive predictive value), negative predictive value, accuracy, F1 score, and area under the receiver operating characteristic curve (AUROC).
Time frame: Up to approximately 1 year after study completion.
Performance of an Artificial Intelligence (AI) algorithm for quantification and longitudinal monitoring of Inherited Retinal Degeneration Associated With Biallelic RPE65- Mutations.
The performance of an AI algorithm will be evaluated for the detection, segmentation, and quantitative assessment of panretinal pathological features (before and after therapy) associated with biallelic RPE65-related retinal degeneration using wide-field color/ pseudocolor fundus images acquired with Optos and Clarus and fundus autofluorescence images acquired from Clarus and Heidelberg engineering imaging systems. Algorithm performance will be assessed using predefined metrics including sensitivity (recall), specificity, precision, negative predictive value, accuracy, F1 score, Dice similarity coefficient, and area under the receiver operating characteristic curve (AUROC), as applicable.
Time frame: Up to approximately 1 year after study completion.
Performance of an Artificial Intelligence (AI) algorithm for quantification and longitudinal monitoring of Inherited Retinal Degeneration Associated With RPGR- Mutations.
The performance of an AI algorithm will be evaluated for the detection, segmentation, and quantitative assessment of panretinal pathological features associated with RPGR-related retinal degeneration using wide-field color/ pseudocolor fundus images acquired with Optos and Clarus and fundus autofluorescence images acquired from Clarus and Heidelberg engineering imaging systems. Algorithm performance will be assessed using predefined metrics including sensitivity (recall), specificity, precision, negative predictive value, accuracy, F1 score, Dice similarity coefficient, and area under the receiver operating characteristic curve (AUROC), as applicable.
Time frame: Up to approximately 1 year after study completion.
Not enrolling at this site.
Plan to share: No — Individual participant-level data will not be made publicly available because the study includes data obtained from multiple participating institutions and is subject to institutional review board requirements, data-use agreements, and participant confidentiality protections.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
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