Artificial intelligence for diagnosis of corneal diseases
Research type
Research Study
Full title
Artificial Intelligence for Diagnosis of Corneal Diseases
IRAS ID
294034
Contact name
Darren Shu Jeng Ting
Contact email
Sponsor organisation
Nottingham University Hospital NHS Trust
Duration of Study in the UK
3 years, 7 months, 31 days
Research summary
Corneal opacity is the 5th leading cause of blindness globally. Any significant injury to the cornea, secondary to infection, inflammation, trauma or degeneration, can lead to corneal scarring with resultant visual impairment. Among all, infectious keratitis (IK) or corneal infection has been shown to be the most common cause of corneal blindness in both developed and developing countries. Successful management of corneal infection is contingent upon timely and accurate diagnosis of the underlying causes. Microbiological investigation such as microscopy, culture and sensitivity testing serves as the current gold standard for diagnosing IK. However, in our recent 12-year study, we observed that the culture yield is only 40%, hindering the subsequent management of the disease. Artificial intelligence has recently emerged as a powerful tool in improving the diagnosis of various ocular conditions, but the potential in diagnosing corneal infection remains unexplored. The aim of this study is to develop an artificial intelligence-based diagnostic tool in improving the diagnostic accuracy of corneal diseases, particularly corneal infection, using ocular images.
REC name
East of Scotland Research Ethics Service REC 1
REC reference
22/ES/0017
Date of REC Opinion
17 Jun 2022
REC opinion
Further Information Favourable Opinion