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Sökning: onr:"swepub:oai:lup.lub.lu.se:6bb82827-12ed-4749-bf5d-a07aa4e52bec" > Can we reduce the w...

  • Rodriguez-Ruiz, AlejandroRadboud University Medical Center,ScreenPoint Medical BV (författare)

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

  • Artikel/kapitelEngelska2019

Förlag, utgivningsår, omfång ...

  • 2019-04-16
  • Springer Science and Business Media LLC,2019

Nummerbeteckningar

  • LIBRIS-ID:oai:lup.lub.lu.se:6bb82827-12ed-4749-bf5d-a07aa4e52bec
  • https://lup.lub.lu.se/record/6bb82827-12ed-4749-bf5d-a07aa4e52becURI
  • https://doi.org/10.1007/s00330-019-06186-9DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:art swepub-publicationtype
  • Ämneskategori:ref swepub-contenttype

Anmärkningar

  • 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.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Lång, KristinaETH Zürich(Swepub:lu)med-ksd (författare)
  • Gubern-Merida, AlbertScreenPoint Medical BV (författare)
  • Teuwen, JonasRadboud University Medical Center (författare)
  • Broeders, MireilleDutch Expert Centre for Screening (LRCB),Radboud University Medical Center (författare)
  • Gennaro, GisellaVeneto Institute of Oncology (författare)
  • Clauser, PaolaMedical University of Vienna (författare)
  • Helbich, Thomas H.Medical University of Vienna (författare)
  • Chevalier, MargaritaComplutense University of Madrid (författare)
  • Mertelmeier, ThomasSiemens Healthineers (författare)
  • Wallis, Matthew G.Cambridge University Hospitals NHS Foundation Trust (författare)
  • Andersson, IngvarLund University,Lunds universitet,Skåne University Hospital(Swepub:lu)ront-ian (författare)
  • Zackrisson, SophiaLund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,Radiology Diagnostics, Malmö,Lund University Research Groups,Skåne University Hospital(Swepub:lu)smi-sza (författare)
  • Sechopoulos, IoannisRadboud University Medical Center (författare)
  • Mann, Ritse M.Radboud University Medical Center (författare)
  • Radboud University Medical CenterScreenPoint Medical BV (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:European Radiology: Springer Science and Business Media LLC29:9, s. 4825-48320938-79941432-1084

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