Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study

A. Rodriguez-Ruiz, K. Lang, A. Gubern-Merida, J. Teuwen, M. Broeders, G. Gennaro, P. Clauser, T. Helbich, M. Chevalier, T. Mertelmeier, M. Wallis, I. Andersson, S. Zackrisson, I. Sechopoulos and R. Mann

European Radiology 2019;29(9):4825-4832.

DOI PMID

To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload. A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gathered from nine previously performed multi-reader multi-case receiver operating characteristic (MRMC ROC) studies. An AI system was used to obtain a score between 1 and 10 for each exam, representing the likelihood of cancer present. Using all AI scores between 1 and 9 as possible thresholds, the exams were divided into groups of low- and high likelihood of cancer present. It was assumed that, under the pre-selection scenario, only the high-likelihood group would be read by radiologists, while all low-likelihood exams would be reported as normal. The area under the reader-averaged ROC curve (AUC) was calculated for the original evaluations and for the pre-selection scenarios and compared using a non-inferiority hypothesis. Setting the low/high-likelihood threshold at an AI score of 5 (high likelihood > 5) results in a trade-off of approximately halving (- 47%) the workload to be read by radiologists while excluding 7% of true-positive exams. Using an AI score of 2 as threshold yields a workload reduction of 17% while only excluding 1% of true-positive exams. Pre-selection did not change the average AUC of radiologists (inferior 95% CI > - 0.05) for any threshold except at the extreme AI score of 9. It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload. * There is potential to use artificial intelligence to automatically reduce the breast cancer screening reading workload by excluding exams with a low likelihood of cancer. * The exclusion of exams with the lowest likelihood of cancer in screening might not change radiologists' breast cancer detection performance. * When excluding exams with the lowest likelihood of cancer, the decrease in true-positive recalls would be balanced by a simultaneous reduction in false-positive recalls.

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