The cornea of the human eye has several functions. It is transparent to allow light into the eye and its shape focuses the incoming light onto the retina. The cornea is responsible for two-thirds of this focusing, while the human lens accounts for the remaining third. Keratoconus is a condition with onset in the second to third decades of life, where the cornea warps into an irregular shape. This irregularity reduces vision due to blurring of the image focused on the retina. Only partial improvement is achievable with traditional glasses because the corneal shape becomes irregular. The glasses prescriptions of patients with keratoconus often differ significantly from the measurements obtained from the cornea in a clinical setting. The predictability of the magnitude and variability of this disparity is poorly understood. As a result, determining the optimal glasses prescription for achieving the best vision correction often involves a time-consuming trial-and-error approach. Improved predictability could reduce the time required to identify the optimal glasses prescription, thereby increasing productivity. For surgical patients, better predictability would enable surgeons to select lenses that provide superior vision outcomes after treatment. In the optometry clinic at the West of England Eye Unit, a database of over 800 patients with glasses prescriptions and corresponding corneal scans has been compiled by the investigators. This is a sufficient dataset to train and assess the prediction accuracy of machine learning models (AI) of glasses measurements using corneal scan parameters as predictor variables.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
The root mean squared error (RMSE) of the machine learning model's predictions for the patient's manifest astigmatism.
Timeframe: Baseline