Inherited Retinal Degeneration Associated With RPGR MutationsMEDIRETNCT07901907
Monitoring and Early Detection of Treatable Inherited RETinal Disease in Children and Adolescents
An observational study of Inherited Retinal Degeneration Associated With RPGR Mutations and Inherited Retinal Degeneration Associated With Biallelic RPE65 Mutations, run by University College, London at 3 sites in 3 countries.
Trial summary
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. Read more about this study
- Who is it for?
- Ages Up to 50 yearsHealthy volunteers can take part
- Conditions the study is for
- Inherited Retinal Degeneration Associated With RPGR Mutations, Inherited Retinal Degeneration Associated With Biallelic RPE65 Mutations
- How many people
- 4,887 participants
- Where
- 3 sitesin 3 countries
- Study dates
- 1 Jan 2026to 31 Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Up to 50 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Study population: 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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
3 sites in 3 countries. Contact the study team or a site near you to ask about taking part.
3 sites in 3 countries
United States1 site, 1 recruiting
- Not yet recruiting
Massachusetts Eye and Ear
Boston, Massachusetts, 02114
Demetrios Vavvas, MD, PhD, 617-573-6359, demetrios_vavvas@meei.harvard.edu
Mohammad Dahrouj, MD, PhD, 617-573-3228, Mohammad_Dahrouj@meei.harvard.edu
Germany1 site
- Completed
TransMIT Centre of Translational Ophthalmology c/o Justus-Liebig-University Giessen
Laubach
United Kingdom1 site, 1 recruiting
- Recruiting
UCL Institute of Ophthalmology
London, EC1V 9EL
Nikolas Pontikos, PhD, MSci, MEng, FBCS, +44 207608 6800, n.pontikos@ucl.ac.uk
Nikolas Pontikos, PhD, MSci, MEng, FBCS
About this 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:
Read more
- Can AI algorithms accurately distinguish patients with RPE65- or RPGR-associated IRDs from individuals without these conditions using ultra-widefield retinal imaging?
- 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.
Read the detailed description
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.
- Conditions
- Inherited Retinal Degeneration Associated With RPGR Mutations, Inherited Retinal Degeneration Associated With Biallelic RPE65 Mutations
- Keywords
- RPGR, AI, detection, monitoring, treatment, inherited retinal degeneration, biallelic RPE65
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Inherited Retinal Degeneration Associated With Biallelic RPE65 Mutations cohort | 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. | |
| Inherited Retinal Degeneration Associated With RPGR Mutations cohort | 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. | |
| Healthy non-myopic control cohort | Participants without myopia and without evidence of inherited retinal degeneration, will be included as one of the two control cohorts. | |
| Myopic control cohort | Participants with myopia, defined as mean spherical equivalent refractive error of ≤ -1.00 dioptres and without evidence of inherited retinal degeneration will be included. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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).
Measured: 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.
Measured: 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.
Measured: Up to approximately 1 year after study completion.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Oct 2026
- Study start1 Jan 2026
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Inherited Retinal Degeneration Associated With RPGR MutationsStudies of the same condition, open and under way.
- More trials run by University College, LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 4,887 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University College, London
- Sponsor type
- University, hospital or other
- Collaborators
- Moorfields Eye Hospital NHS Foundation Trust, TransMIT GmbH c/o Justus Liebig University Giessen, Massachusetts Eye and Ear Infirmary
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07901907
- Sponsor's protocol ID
- EDGE ID 181995
- Other IDs
- MRF-JF-EH-23-122, TRN004, NIHR505133
- Acronym
- MEDIRET
- Status last verified
- Oct 2026
- First submitted
- 6 Oct 2026
- Last updated
- 28 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- 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.
Publications
Birgit Lorenz, Eduardo Dávila-Meza, Sandrine H. Künzel, Frank G Holz, Eduardo Bayro-Corrochano, Alexander Effland; Deep-learning based quantification of RPE65-mutation inherited retinal degeneration from wide-angle images - novel biomarkers for natural history and interventional studies.. Invest. Ophthalmol. Vis. Sci. 2024;65(7):1392.
Pontikos N, Woof WA, Lin S, Ghoshal B, Mendes BS, Veturi A, Nguyen Q, Javanmardi B, Georgiou M, Hustinx A, Ibarra-Arellano MA, Moghul I, Liu Y, Pfau K, Pfau M, Shah M, Yu J, Al-Khuzaei S, Wagner SK, Daich Varela M, Cabral de Guimaraes TA, Sen S, Naik G, Sumodhee D, Fu DJ, Kabiri N, Furman J, Liefers B, Lee AY, De Silva SR, Marques C, Motta F, Fujinami-Yokokawa Y, Hardcastle AJ, Arno G, Lorenz B, Herrmann P, Fujinami K, Sallum J, Madhusudhan S, Downes SM, Holz FG, Balaskas K, Webster AR, Mahroo OA, Krawitz PM, Michaelides M. Next-generation phenotyping of inherited retinal diseases from multimodal imaging with Eye2Gene. Nat Mach Intell. 2025;7(6):967-978. doi: 10.1038/s42256-025-01040-8. Epub 2025 Jun 18.
PubMed 40567353Woof WA, de Guimaraes TAC, Al-Khuzaei S, Daich Varela M, Sen S, Bagga P, Mendes B, Shah M, Burke P, Parry D, Lin S, Naik G, Ghoshal B, Liefers BJ, Fu DJ, Georgiou M, Nguyen Q, Sousa da Silva A, Liu Y, Fujinami-Yokokawa Y, Sumodhee D, Patel P, Furman J, Moghul I, Moosajee M, Sallum J, De Silva SR, Lorenz B, Holz FG, Fujinami K, Webster AR, Mahroo OA, Downes SM, Madhusudhan S, Balaskas K, Michaelides M, Pontikos N. Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of >3500 Patients with Inherited Retinal Disease from the United Kingdom. Ophthalmol Sci. 2024 Nov 12;5(2):100652. doi: 10.1016/j.xops.2024.100652. eCollection 2025 Mar-Apr.
PubMed 39896422Pinedo-Diaz G, Lorenz B, Kunzel SH, Thiele S, Ortega-Cisneros S, Corrochano EB, Holz FG, Effland A. Deep Learning-Based SD-OCT Layer Segmentation Quantifies Outer Retina Changes in Patients With Biallelic RPE65 Mutations Undergoing Gene Therapy. Invest Ophthalmol Vis Sci. 2025 Jan 2;66(1):5. doi: 10.1167/iovs.66.1.5.
PubMed 39745677Chung DC, Bertelsen M, Lorenz B, Pennesi ME, Leroy BP, Hamel CP, Pierce E, Sallum J, Larsen M, Stieger K, Preising M, Weleber R, Yang P, Place E, Liu E, Schaefer G, DiStefano-Pappas J, Elci OU, McCague S, Wellman JA, High KA, Reape KZ. The Natural History of Inherited Retinal Dystrophy Due to Biallelic Mutations in the RPE65 Gene. Am J Ophthalmol. 2019 Mar;199:58-70. doi: 10.1016/j.ajo.2018.09.024. Epub 2018 Sep 28.
PubMed 30268864Wang YZ, Juroch K, Birch DG. Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa. Bioengineering (Basel). 2023 Dec 6;10(12):1394. doi: 10.3390/bioengineering10121394.
PubMed 38135984De Silva SR, Arno G, Robson AG, Fakin A, Pontikos N, Mohamed MD, Bird AC, Moore AT, Michaelides M, Webster AR, Mahroo OA. The X-linked retinopathies: Physiological insights, pathogenic mechanisms, phenotypic features and novel therapies. Prog Retin Eye Res. 2021 May;82:100898. doi: 10.1016/j.preteyeres.2020.100898. Epub 2020 Aug 26.
PubMed 32860923Sassen SH, Sassmannshausen M, Sassen J, Preising MN, Scholz JP, Holz FG, Lorenz B, Herrmann P. Chorioretinal Atrophy after Voretigene Neparvovec Therapy in <italic>RPE65</italic> Mutation-Associated Inherited Retinal Disease: Longitudinal Characterization in Multimodal Imaging. Ophthalmologica. 2026;249(1):30-40. doi: 10.1159/000549548. Epub 2025 Dec 11.
PubMed 41379738Lorenz B. Long-term experience with gene augmentation therapy in patients with inherited retinal disease associated with biallelic mutations in RPE65. Med Genet. 2025 Feb 12;37(1):47-56. doi: 10.1515/medgen-2024-2067. eCollection 2025 Apr.
PubMed 39943983
Study at a glance
From the registry record, last updated by the sponsor on 28 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Monitoring and Early Detection of Treatable Inherited RETinal Disease in Children and Adolescents
- ClinicalTrials.gov ID
- NCT07901907
- Acronym / sponsor ID
- MEDIRET / EDGE ID 181995
- Conditions
- Inherited Retinal Degeneration Associated With RPGR Mutations, Inherited Retinal Degeneration Associated With Biallelic RPE65 Mutations
- Keywords
- RPGR, AI, detection, monitoring, treatment, inherited retinal degeneration, biallelic RPE65
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 4,887 participants (estimated)
- Lead sponsor
- University College, LondonUniversity, hospital or other
- Collaborators
- Moorfields Eye Hospital NHS Foundation Trust, TransMIT GmbH c/o Justus Liebig University Giessen, Massachusetts Eye and Ear Infirmary
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 31 Dec 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 28 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Can AI algorithms accurately distinguish patients with RPE65- or RPGR-associated IRDs from individuals without these conditions using ultra-widefield retinal imaging?
- 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.
Detailed description
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.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Inherited Retinal Degeneration Associated With Biallelic RPE65 Mutations cohort | – | –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. |
| Inherited Retinal Degeneration Associated With RPGR Mutations cohort | – | –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. |
| Healthy non-myopic control cohort | – | –Participants without myopia and without evidence of inherited retinal degeneration, will be included as one of the two control cohorts. |
| Myopic control cohort | – | –Participants with myopia, defined as mean spherical equivalent refractive error of ≤ -1.00 dioptres and without evidence of inherited retinal degeneration will be included. |
Who may qualify
- Ages
- Up to 50 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- 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.
Key inclusion criteria (7 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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
Time frame: Up to approximately 1 year after study completion.
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).
Performance of an Artificial Intelligence (AI) algorithm for the diagnosis of Inherited Retinal Degeneration Associated With RPGR- mutations using Ultra-Wide field retinal imaging
Time frame: Up to approximately 1 year after study completion.
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).
Performance of an Artificial Intelligence (AI) algorithm for quantification and longitudinal monitoring of Inherited Retinal Degeneration Associated With Biallelic RPE65- Mutations.
Time frame: Up to approximately 1 year after study completion.
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.
Performance of an Artificial Intelligence (AI) algorithm for quantification and longitudinal monitoring of Inherited Retinal Degeneration Associated With RPGR- Mutations.
Time frame: Up to approximately 1 year after study completion.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Oct 2026.
- BackgroundBirgit Lorenz, Eduardo Dávila-Meza, Sandrine H. Künzel, Frank G Holz, Eduardo Bayro-Corrochano, Alexander Effland; Deep-learning based quantification of RPE65-mutation inherited retinal degeneration from wide-angle images - novel biomarkers for natural history and interventional studies.. Invest. Ophthalmol. Vis. Sci. 2024;65(7):1392.
- Background: PubMed 40567353Pontikos N, Woof WA, Lin S, Ghoshal B, Mendes BS, Veturi A, Nguyen Q, Javanmardi B, Georgiou M, Hustinx A, Ibarra-Arellano MA, Moghul I, Liu Y, Pfau K, Pfau M, Shah M, Yu J, Al-Khuzaei S, Wagner SK, Daich Varela M, Cabral de Guimaraes TA, Sen S, Naik G, Sumodhee D, Fu DJ, Kabiri N, Furman J, Liefers B, Lee AY, De Silva SR, Marques C, Motta F, Fujinami-Yokokawa Y, Hardcastle AJ, Arno G, Lorenz B, Herrmann P, Fujinami K, Sallum J, Madhusudhan S, Downes SM, Holz FG, Balaskas K, Webster AR, Mahroo OA, Krawitz PM, Michaelides M. Next-generation phenotyping of inherited retinal diseases from multimodal imaging with Eye2Gene. Nat Mach Intell. 2025;7(6):967-978. doi: 10.1038/s42256-025-01040-8. Epub 2025 Jun 18.
- Background: PubMed 39896422Woof WA, de Guimaraes TAC, Al-Khuzaei S, Daich Varela M, Sen S, Bagga P, Mendes B, Shah M, Burke P, Parry D, Lin S, Naik G, Ghoshal B, Liefers BJ, Fu DJ, Georgiou M, Nguyen Q, Sousa da Silva A, Liu Y, Fujinami-Yokokawa Y, Sumodhee D, Patel P, Furman J, Moghul I, Moosajee M, Sallum J, De Silva SR, Lorenz B, Holz FG, Fujinami K, Webster AR, Mahroo OA, Downes SM, Madhusudhan S, Balaskas K, Michaelides M, Pontikos N. Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of >3500 Patients with Inherited Retinal Disease from the United Kingdom. Ophthalmol Sci. 2024 Nov 12;5(2):100652. doi: 10.1016/j.xops.2024.100652. eCollection 2025 Mar-Apr.
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- Individual participant dataSharing planned: 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
3 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Inherited Retinal Degeneration Associated With RPGR Mutations and Inherited Retinal Degeneration Associated With Biallelic RPE65 Mutations, run by University College, London at 3 sites in 3 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 28 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in United States, Germany, United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Nikolas Pontikos, PhD, MSci, MEng, FBCS (+44 207608 6800, n.pontikos@ucl.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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 (time frame: Up to approximately 1 year after study completion.). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07901907, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Oct 2026.