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Can we reduce the w...
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Rodriguez-Ruiz, AlejandroScreenPoint Medical BV,Radboud University Medical Center
(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 ...
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2019-04-16
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Springer Science and Business Media LLC,2019
Nummerbeteckningar
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LIBRIS-ID:oai:lup.lub.lu.se:6bb82827-12ed-4749-bf5d-a07aa4e52bec
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https://lup.lub.lu.se/record/6bb82827-12ed-4749-bf5d-a07aa4e52becURI
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https://doi.org/10.1007/s00330-019-06186-9DOI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:art swepub-publicationtype
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Ämneskategori:ref swepub-contenttype
Anmärkningar
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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 ...)
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Lång, KristinaETH Zürich(Swepub:lu)med-ksd
(författare)
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Gubern-Merida, AlbertScreenPoint Medical BV
(författare)
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Teuwen, JonasRadboud University Medical Center
(författare)
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Broeders, MireilleRadboud University Medical Center,Dutch Expert Centre for Screening (LRCB)
(författare)
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Gennaro, GisellaVeneto Institute of Oncology
(författare)
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Clauser, PaolaMedical University of Vienna
(författare)
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Helbich, Thomas H.Medical University of Vienna
(författare)
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Chevalier, MargaritaComplutense University of Madrid
(författare)
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Mertelmeier, ThomasSiemens Healthineers
(författare)
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Wallis, Matthew G.Cambridge University Hospitals NHS Foundation Trust
(författare)
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Andersson, IngvarLund University,Lunds universitet,Skåne University Hospital(Swepub:lu)ront-ian
(författare)
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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)
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Sechopoulos, IoannisRadboud University Medical Center
(författare)
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Mann, Ritse M.Radboud University Medical Center
(författare)
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ScreenPoint Medical BVRadboud University Medical Center
(creator_code:org_t)
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Ingår i:European Radiology: Springer Science and Business Media LLC29:9, s. 4825-48320938-79941432-1084
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Rodriguez-Ruiz, ...
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Lång, Kristina
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Gubern-Merida, A ...
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Teuwen, Jonas
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Gennaro, Gisella
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Clauser, Paola
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Chevalier, Marga ...
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Mertelmeier, Tho ...
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Wallis, Matthew ...
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Andersson, Ingva ...
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Mann, Ritse M.
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