Imaging and Predictive Modelling of Proliferative Vitreoretinopathy.
Identification of Imaging Biomarkers and Predictive Modelling of Proliferative Vitreoretinopathy Using Deep Learning.
1 other identifier
observational
100
1 country
1
Brief Summary
Patients with retinal detachment are at risk of recurrence and failure of surgery requiring multiple surgeries due to a condition called proliferative vitreoretinopathy (PVR). Study aims to help tailor patients' treatments and improve outcomes by: \[i\] studying imaging biomarkers of PVR, and \[ii\] develop AI models for PVR detection. Inclusion:
- Patients with 'complicated' retinal detachment with PVR recruited to a phase 1 dose-finding trial called MORPH-1.
- Patients with 'simple' retinal detachment without PVR recruited to a PhD study. Non -invasive multimodal imaging and anonymized imaging will be used to study imaging biomarkers of PVR and develop deep learning models to predict PVR in collaboration with an artificial intelligence (AI) expert team at UCL Institute of Ophthalmology.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P50-P75 for all trials
Started Oct 2026
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
May 5, 2026
CompletedFirst Posted
Study publicly available on registry
July 2, 2026
CompletedStudy Start
First participant enrolled
October 12, 2026
ExpectedPrimary Completion
Last participant's last visit for primary outcome
October 15, 2027
Study Completion
Last participant's last visit for all outcomes
October 15, 2027
July 2, 2026
June 1, 2026
1 year
May 5, 2026
June 30, 2026
Conditions
Outcome Measures
Primary Outcomes (1)
To study multimodal imaging biomarkers of PVR.
Use multimodal imaging namely widefield Optos, Widefield OCT, OCT macula, OCT disc, OCT EDI and OCTA to describe biomarkers of PVR.
Preoperative biomarkers of PVR on cases with established PVR Post-operative biomarkers of PVR on cases that develop PVR in the first 3 months.
Secondary Outcomes (1)
To develop deep learning AI models for PVR detection in retinal detachment.
Post-operative 3 months
Study Arms (2)
Rhegmatogenous retinal detachment without proliferative vitreoretinopathy
Rhegmatogenous retinal detachment without proliferative vitreoretinopathy at the time of their primary surgery
Rhegmatogenous retinal detachment with proliferative vitreoretinopathy
Rhegmatogenous retinal detachment with proliferative vitreoretinopathy at the time of their primary surgery or post-operatively with recurrent retinal detachment.
Eligibility Criteria
MORPH-1 - PVR detachments Cohort-NHS - primary RRD without PVR requiring PPV with PVD.
You may qualify if:
- MORPH-1 and Cohort-NHS study imaging
You may not qualify if:
- Participants from above studies who have not consented for image analysis and AI related analysis.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
University College London
London, United Kingdom
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
James Bainbridge
University College, London
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Target Duration
- 3 Months
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
May 5, 2026
First Posted
July 2, 2026
Study Start (Estimated)
October 12, 2026
Primary Completion (Estimated)
October 15, 2027
Study Completion (Estimated)
October 15, 2027
Last Updated
July 2, 2026
Record last verified: 2026-06
Data Sharing
- IPD Sharing
- Will not share
Within participant's consent.