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.
Study Type
OBSERVATIONAL
Enrollment
100
University College London
London, United Kingdom
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.
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.
To develop deep learning AI models for PVR detection in retinal detachment.
Use widefield Optos imaging and OCT to: * Develop outputs for presence of PVR (binary), and * Develop output for prediction of PVR grade.
Time frame: Post-operative 3 months
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