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

Eur Radiol. 2019 Sep;29(9):4825-4832. doi: 10.1007/s00330-019-06186-9. Epub 2019 Apr 16.

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.

Keywords: Artificial intelligence; Breast cancer; Deep learning; Mammography; Screening.

MeSH terms

  • Artificial Intelligence*
  • Breast Neoplasms / diagnostic imaging*
  • Early Detection of Cancer / methods*
  • False Negative Reactions
  • False Positive Reactions
  • Feasibility Studies
  • Female
  • Humans
  • Mammography / methods*
  • Mass Screening / methods
  • Probability
  • ROC Curve
  • Radiologists
  • Workload