A deep-learning algorithm increased the accuracy of reading images for detecting retinal pathologies in patients with diabetes.
The first of which is how retina specialists and society will begin to trust machine learning—i.e., how these algorithms arrive at their decisions or predictions is not currently transparent to the operator. In a future in which humans will co-exist with machines, some degree of human oversight will still be required and the manner in which that will take place remains to be determined.
Dr. Rahimy and colleagues conducted a study to understand the impact of deep-learning DR algorithms on physician-readers in computer-assisted settings.
In this study, the computer both scored the histograms and provided weighted averages that the algorithm favored in the prediction of a diagnosis based on an image and provided heatmaps with areas of highlighting that most contribute to the prediction, he noted.
The study, which included 10 physicians who read 1,796 images, had three scenarios in which the readers were:
- unassisted in reading the images and determining a diagnosis;
- assisted by the grades provided and favored by the algorithm, or
- assisted by both the grades and the heatmaps provided by the algorithm, Dr. Rahimy explained.
The images, which were provided by EyePACS (a tele-ophthalmology provider), were representative of a DR population, in that DR was not present in 70%. Of the 10 readers, five were general ophthalmologists, four were retina specialists, and one was a retina fellow who recently completed training.
Ehsan Rahimy, MD
E: [email protected]
Dr. Rahimy is a physician consultant to Google.