Over this past summer, I was fortunate enough to be given the opportunity to deliver a speech to the State University of New York (SUNY) College of Optometry residency class of 2019. During this 20 minutes (which they likely perceived as just over an hour), I recommended that residents take a few moments and conduct a search of the world's literature using the key words "deep learning" with the disease of their choice. Conducting such a search myself gave me a better understanding of the likely direction of health care in my clinical lifetime. A study recently published in JAMA Ophthalmology describes a deep learning system which appears to show high sensitivity and specificity for the detection of glaucoma.1 Previously by Dr. Casella: Consider IOP fluctuations when diagnosing glaucoma Deep learning So, just what exactly is deep learning? In the arena of artificial intelligence, this subset of machine learning is based on so-called "neural networks" that process data into concepts.

Results From a total of 274 413 fundus images initially obtained from CGSA, 269 601 images passed initial image quality review and were graded for GON. A total of 241 032 images (definite GON 29 865 [12.4%], probable GON 11 046 [4.6%], unlikely GON 200 121 [83%]) from 68 013 patients were selected using random sampling to train the GD-CNN model. Validation and evaluation of the GD-CNN model was assessed using the remaining 28 569 images from CGSA. The AUC of the GD-CNN model in primary local validation datasets was 0.996 (95% CI, 0.995-0.998), The most common reason for both false-negative and false-positive grading by GD-CNN (51 of 119 [46.3%] and 191 of 588 [32.3%]) and manual grading (50 of 113 [44.2%] and 183 of 538 [34.0%]) was pathologic or high myopia.