Abstract: Methods, systems, and apparatus for illumination of the cornea of an eye are disclosed; in some implementations, such methods, systems, and apparatus may generally comprise or employ a light source and an optical mask, alignment of a viewing portal, and capture of a cornea-reflected image using a camera or other optical array. In some implementations, a system and method may generally comprise or involve generating data that are optimized for ingestion by an artificial intelligence or machine learning engine, thereby facilitating contact lens selection or detection of keratoconus or “dry eye” conditions for ophthalmic, optometric, and pediatric practices. Specifically, the disclosed subject matter teaches innovative cornea-reflective patterns, the reflections of which are designed for ingestion by statistical, machine learning, or artificial intelligence models.

Abstract: A computer-implemented method for identifying one or more patients at risk of having an undetected ophthalmic condition is described. The method may make use of non-ophthalmic data; pre-process the data to generate a culled dataset. The model may be trained and tested based on separate portions of the culled dataset. Finally the model may output, based on the analyzing the data, an indication of the existence or non-existence of one or more ophthalmic conditions.