A deep-learning algorithm increased the accuracy of reading images for detecting retinal pathologies in patients with diabetes.
Dr. Rahimy reported that for moderate nonproliferative DR, when readers were unassisted the mean sensitivity was 79.4%. With grades only, the sensitivity rose to 87.5%, and with grades and heatmaps, it increased further to 88.7%.
The increase in sensitivity did not occur with a drop in the specificity. For the same pathology, the respective percentages of specificity were 96.6%, 96.1%, and 95.5%.
When the graders were questioned about their confidence in computer-assisted reading, the readers reported increasing levels of confidence as they moved from the unassisted scenario to the grading assistance to grading and heatmap assistance.
Another finding was that the time in seconds required to reach a diagnosis increased with the added levels of assistance regardless of whether DR was present. The readers were not informed before the study that the time to diagnosis was measured.
“Over the course of the experiment, the time spent on the task decreased across all conditions, suggesting that there was a learning curve,” Dr. Rahimy said.
“Assistance with a deep-learning algorithm can improve the accuracy of and the confidence in the diagnosis of DR and prevent underdiagnosis by improving sensitivity with little to no loss of specificity,” he concluded. “The effects depend on the background of the reader. In some cases, doctor plus assistance is greater than either alone.”
Ehsan Rahimy, MD
E: [email protected]
Dr. Rahimy is a physician consultant to Google.