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Can we reduce the w...
Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study
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- Rodriguez-Ruiz, Alejandro (författare)
- ScreenPoint Medical BV,Radboud University Medical Center
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- Lång, Kristina (författare)
- ETH Zürich
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- Gubern-Merida, Albert (författare)
- ScreenPoint Medical BV
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- Teuwen, Jonas (författare)
- Radboud University Medical Center
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- Broeders, Mireille (författare)
- Radboud University Medical Center,Dutch Expert Centre for Screening (LRCB)
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- Gennaro, Gisella (författare)
- Veneto Institute of Oncology
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- Clauser, Paola (författare)
- Medical University of Vienna
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- Helbich, Thomas H. (författare)
- Medical University of Vienna
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- Chevalier, Margarita (författare)
- Complutense University of Madrid
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- Mertelmeier, Thomas (författare)
- Siemens Healthineers
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- Wallis, Matthew G. (författare)
- Cambridge University Hospitals NHS Foundation Trust
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- Andersson, Ingvar (författare)
- Lund University,Lunds universitet,Skåne University Hospital
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- Zackrisson, Sophia (författare)
- Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,Radiology Diagnostics, Malmö,Lund University Research Groups,Skåne University Hospital
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- Sechopoulos, Ioannis (författare)
- Radboud University Medical Center
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- Mann, Ritse M. (författare)
- Radboud University Medical Center
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(creator_code:org_t)
- 2019-04-16
- 2019
- Engelska.
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 29:9, s. 4825-4832
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://link.springe...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- Artificial intelligence
- Breast cancer
- Deep learning
- Mammography
- Screening
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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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Broeders, Mireil ...
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Gennaro, Gisella
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visa fler...
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Clauser, Paola
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Helbich, Thomas ...
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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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Zackrisson, Soph ...
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Sechopoulos, Ioa ...
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Mann, Ritse M.
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