Imaging and Predictive Modelling of Proliferative Vitreoretinopathy.
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Proliferative Vitreoretinopathy. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United Kingdom
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Identification of Imaging Biomarkers and Predictive Modelling of Proliferative Vitreoretinopathy Using Deep Learning.
Overview
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.
Primary outcome measures
- To study multimodal imaging biomarkers of PVR. [Time frame: 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 outcome measures (1)
- To develop deep learning AI models for PVR detection in retinal detachment. [Time frame: Post-operative 3 months]
Eligibility criteria
Inclusion criteria
- MORPH-1 and Cohort-NHS study imaging
Exclusion criteria
- Participants from above studies who have not consented for image analysis and AI related analysis.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
United Kingdom · 1 center
- University College London — London
Identifiers
NCT: NCT07682025 · EDGE 183954