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

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift


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.


  • Alejandro Rodriguez-Ruiz
  • Kristina Lång
  • Albert Gubern-Merida
  • Jonas Teuwen
  • Mireille Broeders
  • Gisella Gennaro
  • Paola Clauser
  • Thomas H. Helbich
  • Margarita Chevalier
  • Thomas Mertelmeier
  • Matthew G. Wallis
  • Ingvar Andersson
  • Sophia Zackrisson
  • Ioannis Sechopoulos
  • Ritse M. Mann
Enheter & grupper
Externa organisationer
  • ETH Zürich
  • Veneto Institute of Oncology
  • Medical University of Vienna
  • Complutense University of Madrid
  • Cambridge University Hospitals NHS Foundation Trust
  • Skåne University Hospital
  • ScreenPoint Medical BV
  • Radboud University Medical Center
  • Dutch Expert Centre for Screening (LRCB)
  • Siemens Healthineers

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Radiologi och bildbehandling


Sidor (från-till)4825-4832
TidskriftEuropean Radiology
Utgåva nummer9
Tidigt onlinedatum2019 apr 16
StatusPublished - 2019
Peer review utfördJa