Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.
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 gath...
| Publicado en: | European Radiology Vol. 29; no. 9; pp. 4825 - 4833 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137908435&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137908435 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Sep2019 vid: 29 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137908435 137908435 NLM30993432 137908435 10.1007/s00330-019-06186-9 NLM30993432 137908435 ppf: 4825 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. aug: au: Rodriguez-Ruiz, Alejandro Lång, Kristina Gubern-Merida, Albert Teuwen, Jonas Broeders, Mireille Gennaro, Gisella Clauser, Paola Helbich, Thomas H. Chevalier, Margarita Mertelmeier, Thomas Wallis, Matthew G. Andersson, Ingvar Zackrisson, Sophia Sechopoulos, Ioannis Mann, Ritse M. affil: Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands sug: subj: Mammography Methods Early Detection of Cancer Methods Artificial Intelligence Breast Neoplasms Probability Pilot Studies False Positive Results False Negative Results ROC Curve Workload Health Screening Methods Female Human Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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