Artificial Intelligence Analysis of Myopic Vitreoretinal Pathology
Description: PROJECT NARRATIVE: Currently, no methods exist to prevent the onset of retinal tears and subsequent rhegmatogenous retinal detachments except to caution patients of new floaters, flashes of light and curtains in their vision. In spite of these warnings, patients may present late after the onset of symptoms resulting in poorer visual outcomes even after detachment repair. In this proposal, Dr. Cassie Ludwig aims to determine whether the use of clinical structured and unstructured variables in the electronic health record in addition to ultra-widefield color fundus photography can aid in improving prediction of risk of tears and detachments in myopic and non-myopic patients and thereby allow physicians to consider early prophylactic treatments.
Funder: National Eye Institute
Funding type: career_development
Funder scheme: K23
Start date: 2024-03-01
End date: 2029-02-28
Investigator: CHASE AXEL LUDWIG
Institution: STANFORD UNIVERSITY
Funded outputs count: 19
Funder award ID: K23EY035741
OpenAlex ID: G9000863578