Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment

被引:0
作者
Si Eun Lee
Nak-Hoon Son
Myung Hyun Kim
Eun-Kyung Kim
机构
[1] Yongin Severance Hospital,Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science
[2] Yonsei University College of Medicine,Division of Biostatistics
[3] Yongin Severance Hospital,undefined
[4] Yonsei University College of Medicine,undefined
来源
Journal of Digital Imaging | 2022年 / 35卷
关键词
Breast density; Digital mammography; Diagnosis, Computer-assisted; Artificial intelligence;
D O I
暂无
中图分类号
学科分类号
摘要
We evaluated and compared the mammographic density assessment of an artificial intelligence-based computer-assisted diagnosis (AI-CAD) program using inter-rater agreements between radiologists and an automated density assessment program. Between March and May 2020, 488 consecutive mammograms of 488 patients (56.2 ± 10.9 years) were collected from a single institution. We assigned four classes of mammographic density based on BI-RADS (Breast Imaging Reporting and Data System) using commercial AI-CAD (Lunit INSIGHT MMG), and compared inter-rater agreements between radiologists, AI-CAD, and another commercial automated density assessment program (Volpara®). The inter-rater agreement between AI-CAD and the reader consensus was 0.52 with a matched rate of 68.2% (333/488). The inter-rater agreement between Volpara® and the reader consensus was similar to AI-CAD at 0.50 with a matched rate of 62.7% (306/488). The inter-rater agreement between AI-CAD and Volpara® was 0.54 with a matched rate of 61.5% (300/488). In conclusion, density assessments by AI-CAD showed fair agreement with those of radiologists, similar to the agreement between the commercial automated density assessment program and radiologists.
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页码:173 / 179
页数:6
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