Further adventures in AI-directed double reading (AID-DR) for single reading environments

Abstract

Previously, we proposed a method to use an artificial intelligent (AI) tool to integrate double reading into a single reading environment (AI-directed double reading, AID-DR). The AI tool scores each case offline, and the score is compared to the radiologist’s recall recommendation. If the AI score is above the high threshold and the radiologist did not recall the woman or if the score is below the low threshold and the radiologist recommended recall, then the case is sent to a second radiologist. In this presentation, we examined the effect of selecting the second radiologist and the low and high threshold values. We found that if the second radiologist has a high recall rate, then there is little benefit to AID-DR. However, if the second has a low recall rate, then there is benefit of slightly higher sensitivity with a lower overall recall rate for AIDDR compared to single reading, without requiring the second radiologist to read many cases. Both threshold values will affect the overall sensitivity and specificity with the biggest gains in specificity.

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