Going from double to single reading for screening exams labeled as likely normal by AI: what is the impact?

Abstract

We investigated whether a deep learning-based artificial intelligence (AI) system can be used to improve breast cancer screening workflow efficiency by making a pre-selection of likely normal screening mammograms where double-reading could be safely replaced with single-reading. We collected 18,015 consecutively acquired screening exams, the independent reading assessments by each radiologist of the double reading process, and the information about whether the case was recalled and if so the recall outcome. The AI system assigned a 1-10 score to each screening exam denoting the likelihood of cancer. We simulated the impact on recall rate, cancer detection rate, and workload if single-reading would have been performed for the mammograms with the lowest AI scores. After evaluating all possible AI score thresholds, it was found that when AI scores 1 to 7 are single read instead of double read, the cancer detection rate would have remained the same (no screen-detected cancers missed –the AI score is low but the single-reader would recall the exam), recall rate would have decreased by 11.8% (from 5.35% to 4.79%), and screen reading workload would have decreased by 32.6%. In conclusion, using an AI system could improve breast cancer screening efficiency by pre-selecting likely normal exams where double-reading might not be needed.

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