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Eur Radiol. 2019 Apr 16. doi: 10.1007/s00330-019-06186-9. [Epub ahead of print]

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

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Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
ScreenPoint Medical BV, Stationplein 26, 6512 AB, Nijmegen, The Netherlands.
Institute for Biomedical Engineering, ETH Zurich, Gloriastrasse 35, 8092, Zürich, Switzerland.
Department for Health Evidence, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, The Netherlands.
Veneto Institute of Oncology (IOV)-IRCCS, via Gattamelata 64, 35128, Padua, Italy.
Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Medical Physics Group, Radiology Department, Faculty of Medicine, Universidad Complutense de Madrid, Pza. Ramón y Cajal s/n, 28040, Madrid, Spain.
Siemens Healthcare GmbH, Diagnostic Imaging, X-Ray Products, Technology & Concepts, Siemensstr. 3, 91301, Forchheim, Germany.
Cambridge Breast Unit and NIHR Biomedical Research Unit, Box 97, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
Unilabs Breast Center, Skåne University Hospital, Jan Waldenströms gata 22, SE-20502, Malmö, Sweden.
Diagnostic Radiology, Department of Translational Medicine, Lund University, Skåne University Hospital, SE-20502, Malmö, Sweden.
Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.



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.


Artificial intelligence; Breast cancer; Deep learning; Mammography; Screening


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