Ophthalmic instruments are constantly improving and can now generate massive volumes of complex diagnostic images. While the data contained in these images exceeds human analytical capabilities, they’re driving new developments and applications of AI.
Deep-learning models, which have been “trained” using huge volumes of images, have already revealed links between phenomena. Using a single image of the back of the eye, these models can determine a patient’s gender, age, smoking history, blood pressure and cardiovascular risk factors.
Now, innovations in ophthalmic image analysis are showing promise for a new area of precision medicine. Specifically, treatment of age-related macular degeneration could be guided and customized based on predictions of an individual’s response and the risk that a mild condition could deteriorate into a more severe form of the disease. Recent breakthroughs are establishing correlations between how patients respond to treatment and which layers of the retina have been affected by their condition.
Other algorithms will soon be able to help with real-time diagnoses and decision-making, making it easier to perform mass screening and provide medical care for diabetic retinopathy at the most appropriate stage of the disease. The theoretical possibility of evaluating up to 260 million images per day using an automated system is one of the most promising avenues of further study.