Search: id:"swepub:oai:lup.lub.lu.se:6bb82827-12ed-4749-bf5d-a07aa4e52bec" >
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
-
- Rodriguez-Ruiz, Alejandro (author)
- Radboud University Medical Center,ScreenPoint Medical BV
-
- Lång, Kristina (author)
- ETH Zürich
-
- Gubern-Merida, Albert (author)
- ScreenPoint Medical BV
-
show more...
-
- Teuwen, Jonas (author)
- Radboud University Medical Center
-
- Broeders, Mireille (author)
- Dutch Expert Centre for Screening (LRCB),Radboud University Medical Center
-
- Gennaro, Gisella (author)
- Veneto Institute of Oncology
-
- Clauser, Paola (author)
- Medical University of Vienna
-
- Helbich, Thomas H. (author)
- Medical University of Vienna
-
- Chevalier, Margarita (author)
- Complutense University of Madrid
-
- Mertelmeier, Thomas (author)
- Siemens Healthineers
-
- Wallis, Matthew G. (author)
- Cambridge University Hospitals NHS Foundation Trust
-
- Andersson, Ingvar (author)
- Lund University,Lunds universitet,Skåne University Hospital
-
- Zackrisson, Sophia (author)
- Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,Radiology Diagnostics, Malmö,Lund University Research Groups,Skåne University Hospital
-
- Sechopoulos, Ioannis (author)
- Radboud University Medical Center
-
- Mann, Ritse M. (author)
- Radboud University Medical Center
-
show less...
-
(creator_code:org_t)
- 2019-04-16
- 2019
- English.
-
In: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 29:9, s. 4825-4832
- Related links:
-
http://dx.doi.org/10... (free)
-
show more...
-
https://link.springe...
-
https://lup.lub.lu.s...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Keyword
- Artificial intelligence
- Breast cancer
- Deep learning
- Mammography
- Screening
Publication and Content Type
- art (subject category)
- ref (subject category)
Find in a library
To the university's database
- By the author/editor
-
Rodriguez-Ruiz, ...
-
Lång, Kristina
-
Gubern-Merida, A ...
-
Teuwen, Jonas
-
Broeders, Mireil ...
-
Gennaro, Gisella
-
show more...
-
Clauser, Paola
-
Helbich, Thomas ...
-
Chevalier, Marga ...
-
Mertelmeier, Tho ...
-
Wallis, Matthew ...
-
Andersson, Ingva ...
-
Zackrisson, Soph ...
-
Sechopoulos, Ioa ...
-
Mann, Ritse M.
-
show less...
- About the subject
-
- MEDICAL AND HEALTH SCIENCES
-
MEDICAL AND HEAL ...
-
and Clinical Medicin ...
-
and Radiology Nuclea ...
- Articles in the publication
-
European Radiolo ...
- By the university
-
Lund University