Machine learning heads off unwise eye surgeries

Eyesight researchers have developed a machine-learning architecture whose best model, an ensemble classifier, achieved 93.4% accuracy in separating good candidates for corneal refractive surgery from patients likely to have post-surgery complications or poor outcomes.

The model performed much better than conventional screening methods and as well as highly experienced ophthalmologists who participated in the research.

The full study was published with open access in npj Digital Medicine.

Senior author Tyler Hyungtaek Rim of Singapore Eye Research Institute and colleagues trained five algorithms to identify appropriate surgery candidates.