ABSTRACT
In the last few years, machine learning has made significant strides in various aspects of healthcare,including the field of ophthalmology. In ophthalmology, a majority of research on the integration of machine learning has centered on the diagnosis of diabetic retinopathy and age-related macular degeneration but it has also been used for other ophthalmological conditions, including glaucoma, cataracts, and retinopathy of prematurity. Also, outside of image detection, machine learning is useful in identifying risk factors for predicting the severity of ophthalmological diseases. Based on statistical measures such as the area under the curve, sensitivity, and specificity, the neural networks indicate excellent performance in detecting many of the ophthalmology disease processes. In 2018, the Food and Drug Administration approved the use of an artificial intelligence system in the detection of diabetic retinopathy and it is expected that as the deep learning models continue to improve, their adoption into the real-world healthcare setting will continue to grow.
