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Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography

Authors
 Si Eun Lee  ;  Ga Ram Kim  ;  Jung Hyun Yoon  ;  Kyunghwa Han  ;  Won Jeong Son  ;  Hye Jung Shin  ;  Hee Jung Moon 
Citation
 ACTA RADIOLOGICA, Vol.64(5) : 1808-1815, 2023-05 
Journal Title
ACTA RADIOLOGICA
ISSN
 0284-1851 
Issue Date
2023-05
MeSH
Artificial Intelligence* ; Breast / diagnostic imaging ; Breast Density ; Breast Neoplasms* / diagnostic imaging ; Early Detection of Cancer ; Female ; Humans ; Mammography ; Retrospective Studies
Keywords
Digital mammography ; artificial intelligence ; breast neoplasms ; computer-assisted ; diagnosis
Abstract
Background: Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views.

Purpose: To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view.

Material and Methods: From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included.

Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

Results: Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance (P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P =0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P =0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P<0.001) significantly improved with AI assistance.

Conclusion: AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.
Full Text
https://journals.sagepub.com/doi/10.1177/02841851221140556
DOI
10.1177/02841851221140556
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Yoon, Jung Hyun(윤정현) ORCID logo https://orcid.org/0000-0002-2100-3513
Lee, Si Eun(이시은) ORCID logo https://orcid.org/0000-0002-3225-5484
Han, Kyung Hwa(한경화)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/195388
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