Imaging and predictive modelling of proliferative vitreoretinopathy V1
Research type
Research Study
Full title
Identification of imaging biomarkers and predictive modelling of proliferative vitreoretinopathy using deep learning.
IRAS ID
360601
Contact name
Mahiul MK Muqit
Contact email
Sponsor organisation
University College London
Clinicaltrials.gov Identifier
Z6364106/2025/07/145, UCL DPO
Duration of Study in the UK
1 years, 0 months, 4 days
Research summary
Proliferative vitreoretinopathy (PVR) complicating rhegmatogenous retinal detachments (RRD) remain a significant cause of surgical failure despite advances in surgical instrumentation and techniques. However, despite major advances in freely accessible and non-invasive imaging techniques in the recent years, we have yet to identify if imaging biomarkers can help predict PVR in day-to-day clinical practice, therefore help tailor patients’ treatments and improve outcomes for them.
This is a PhD study to investigate imaging biomarkers of PVR from multimodal imaging techniques and develop deep learning models to predict PVR in collaboration with an artificial intelligence (AI) expert team at UCL Institute of Ophthalmology.
This study will use anonymised imaging obtained from two separate studies:
1. Cohort-MMC - phase 1 dose-finding trial called MORPH-1 as part of its ancillary studies to study 'complicated' RRD due to PVR.
2. Cohort-NHS - control set of imaging obtained from patients with uncomplicated ‘simple’ RRD.
2. Cohort-fellow eye - another control set of fellow eyes of participants without RD or PVR.Above imaging will be obtained at Moorfields Eye Hospital Clinical research Facility and anonymised imaging data will be analysed at UCL.
REC name
Wales REC 4
REC reference
26/WA/0092
Date of REC Opinion
10 Apr 2026
REC opinion
Favourable Opinion