Clinical Research Directory

Monitoring and Early Detection of Treatable Inherited RETinal Disease in Children and Adolescents

The goals of this observational study are to develop and train artificial intelligence (AI) algorithms using multimodal imaging to identify patients with inherited retinal degeneration (IRD) caused by Retinal Pigment Epithelium Specific Protein 65 (RPE65) and Retinitis Pigmentosa GTPase Regulator (RPGR) mutations. Another algorithm will be developed to measure and monitor retinal changes over time, with the aim of helping identify patients who may be suitable for available or emerging treatments and supporting assessment of disease progression and treatment response. The main questions this study aims to answer are: 1. Can AI algorithms accurately distinguish patients with RPE65- or RPGR-associated IRDs from individuals without these conditions using ultra-widefield retinal imaging? 2. Can AI algorithms accurately detect and quantify retinal abnormalities associated with these conditions and monitor their progression over time on ultra-widefield retinal imaging? Researchers will compare retinal images from patients with RPE65- or RPGR-associated IRDs with images from healthy participants, including people with and without myopia, to assess how accurately the algorithms can distinguish between these groups and monitor disease-related changes.