Identification of Imaging Biomarkers and Predictive Modelling of Proliferative Vitreoretinopathy Using Deep Learning.
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- To study multimodal imaging biomarkers of PVR.
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •MORPH-1 and Cohort-NHS study imaging
排除标准
- •Participants from above studies who have not consented for image analysis and AI related analysis.
结局指标
主要结局
To study multimodal imaging biomarkers of PVR.
时间窗: 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.
次要结局
- To develop deep learning AI models for PVR detection in retinal detachment.(Post-operative 3 months)