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

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Title: Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.
Authors: Rodriguez-Ruiz, Alejandro1,2, Lång, Kristina3, Gubern-Merida, Albert2, Teuwen, Jonas1, Broeders, Mireille4,5, Gennaro, Gisella6, Clauser, Paola7, Helbich, Thomas H.7, Chevalier, Margarita8, Mertelmeier, Thomas9, Wallis, Matthew G.10, Andersson, Ingvar11, Zackrisson, Sophia12, Sechopoulos, Ioannis1,5, Mann, Ritse M.1 Ritse.Mann@radboudumc.nl
Source: European Radiology. Sep2019, Vol. 29 Issue 9, p4825-4832. 8p. 1 Black and White Photograph, 3 Graphs.
Subjects: Artificial intelligence, Automatic identification, Receiver operating characteristic curves, Early detection of cancer, Breast tumors, Probability theory, Pilot projects, Diagnostic errors, Mammograms, Medical screening, Employees' workload
Abstract: Purpose: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload.Methods and Materials: 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.Results: 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.Conclusion: It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload.Key Points: • 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. [ABSTRACT FROM AUTHOR]
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Abstract:<bold>Purpose: </bold>To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload.<bold>Methods and Materials: </bold>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.<bold>Results: </bold>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.<bold>Conclusion: </bold>It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload.<bold>Key Points: </bold>• 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. [ABSTRACT FROM AUTHOR]
ISSN:09387994
DOI:10.1007/s00330-019-06186-9