Imaging and Predictive Modelling of Proliferative Vitreoretinopathy.

Identification of Imaging Biomarkers and Predictive Modelling of Proliferative Vitreoretinopathy Using Deep Learning.

Status

Enrolling by invitation

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07682025

Enrollment

100

Registered

2026-07-02

Start date

2026-10-12

Completion date

2027-10-15

Last updated

2026-07-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Proliferative Vitreoretinopathy

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
To study multimodal imaging 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.	Use multimodal imaging namely widefield Optos, Widefield OCT, OCT macula, OCT disc, OCT EDI and OCTA to describe biomarkers of PVR.

Secondary

Measure	Time frame	Description
To develop deep learning AI models for PVR detection in retinal detachment.	Post-operative 3 months	Use widefield Optos imaging and OCT to: * Develop outputs for presence of PVR (binary), and * Develop output for prediction of PVR grade.

Countries

United Kingdom

Contacts

PRINCIPAL_INVESTIGATORJames Bainbridge

University College, London

Outcome results