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Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow

 Jung Hyun Yoon  ;  Kyungwha Han  ;  Hee Jung Suh  ;  Ji Hyun Youk  ;  Si Eun Lee  ;  Eun-Kyung Kim 
 EUROPEAN JOURNAL OF RADIOLOGY OPEN, Vol.11 : 100509, 2023-07 
Journal Title
Issue Date
Artificial intelligence ; Breast cancer screening ; Computer-assisted detection ; Computer-assisted diagnosis ; Mammography
Purpose: To evaluate the stand-alone diagnostic performances of AI-CAD and outcomes of AI-CAD detected abnormalities when applied to the mammographic interpretation workflow. Methods: From January 2016 to December 2017, 6499 screening mammograms of 5228 women were collected from a single screening facility. Historic reads of three radiologists were used as radiologist interpretation. A commercially-available AI-CAD was used for analysis. One radiologist not involved in interpretation had retrospectively reviewed the abnormality features and assessed the significance (negligible vs. need recall) of the AI-CAD marks. Ground truth in terms of cancer, benign or absence of abnormality was confirmed according to histopathologic diagnosis or negative results on the next-round screen. Results: Of the 6499 mammograms, 6282 (96.7%) were in the negative, 189 (2.9%) were in the benign, and 28 (0.4%) were in the cancer group. AI-CAD detected 5 (17.9%, 5 of 28) of the 9 cancers that were intially interpreted as negative. Of the 648 AI-CAD recalls, 89.0% (577 of 648) were marks seen on examinations in the negative group, and 267 (41.2%) of the AI-CAD marks were considered to be negligible. Stand-alone AI-CAD has significantly higher recall rates (10.0% vs. 3.4%, P < 0.001) with comparable sensitivity and cancer detection rates (P = 0.086 and 0.102, respectively) when compared to the radiologists’ interpretation. Conclusion: AI-CAD detected 17.9% additional cancers on screening mammography that were initially overlooked by the radiologists. In spite of the additional cancer detection, AI-CAD had significantly higher recall rates in the clinical workflow, in which 89.0% of AI-CAD marks are on negative mammograms. © 2023 The Authors
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1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
6. Others (기타) > Dept. of Health Promotion (건강의학과) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Suh, Hee Jung(서희정)
Youk, Ji Hyun(육지현) ORCID logo https://orcid.org/0000-0002-7787-780X
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)
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