In broad terms, any new technological development consists of two categories worth watching: the ongoing development of the technology itself, and the expanding uses of it.  AI in healthcare is no different.

For example, in a recent study, eyesight researchers have developed a machine-learning architecture whose best model achieved 93.4% accuracy in separating good candidates for corneal refractive surgery – an eye surgery used to improve the refractive state of the eye and decrease or eliminate dependency on glasses or contact lenses, the most common of which is LASIK surgery – from patients likely to have post-surgery complications or poor outcomes.

According to the study, which was published in npj Digital Medicine, the model performed much better than conventional screening methods and as well as highly experienced ophthalmologists who participated in the research.  

Until recently, senior author Tyler Hyungtaek Rim of Singapore Eye Research Institute and colleagues wrote in the report, there has been no definitive screening to confront the possibility of a misdiagnosis in candidates for corneal refractive surgery.  For this study, “a machine learning architecture was built with the aim of identifying candidates combining the large multi-instrument data from patients and clinical decisions of highly experienced experts. Five heterogeneous algorithms were used to predict candidates for surgery.”

From these overlapping tools researchers built the high-performing ensemble classifier, training it on corneal tomography images, demographics data and data from other ophthalmic examinations of more than 10,000 patients.

The team internally validated the approach on data from more than 2,600 patients and subsequently validated it externally using data from more than 5,200 patients who received screening exams after the architecture’s development period.

“Our proposed machine learning model is expected to perform reliably because it was trained by a large population,” the authors concluded. “An automated analysis of preoperative data can provide a safe and reliable clinical decision for refractive surgery. In the future, this approach will facilitate standardized and automated selections of surgical choices.”