The Next Frontier of Imaging in Ophthalmology: Machine Learning and Tissue Biomechanics

Abstract

Medical imaging has revolutionized the diagnosis and management of disease in healthcare. Early integration of computers into medical imaging brought control of devices and data acquisition to new heights of precision. As computing power and sophistication of software evolve, we have reached an era of computer-based image interpretation. Machine learning approaches have been developed to automate certain quantitative measures derived from these modalities. Prospects for machine learning in ophthalmology address its potential role in approaching some of the most common causes of blindness worldwide: diabetic retinopathy, glaucoma, and age-related macular degeneration. As these analysis techniques evolve, concurrent advancements are seen in ophthalmology imaging technologies themselves, and today, the aqueous outflow tract and the optic nerve head can be visualized in more detail than ever before. The optimization and assimilation of these tools may hold clinically significant answers to screening, diagnosing, and managing disease.