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

Author:

Lee Si Eun1,Kim Ga Ram2,Yoon Jung Hyun2,Han Kyunghwa2,Son Won Jeong3,Shin Hye Jung3,Moon Hee Jung4ORCID

Affiliation:

1. Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea

2. Department of Radiology, Research Institute of Radiologic Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

3. Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea

4. Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea

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.

Publisher

SAGE Publications

Subject

Radiology, Nuclear Medicine and imaging,General Medicine,Radiological and Ultrasound Technology

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