Artikel ; Online: Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time.
Radiology. Artificial intelligence
2024 Band 6, Heft 3, Seite(n) e230318
Abstract: Purpose To develop an artificial intelligence (AI) model for the diagnosis of breast cancer on digital breast tomosynthesis (DBT) images and to investigate whether it could improve diagnostic accuracy and reduce radiologist reading time. Materials and ... ...
| Abstract | Purpose To develop an artificial intelligence (AI) model for the diagnosis of breast cancer on digital breast tomosynthesis (DBT) images and to investigate whether it could improve diagnostic accuracy and reduce radiologist reading time. Materials and Methods A deep learning AI algorithm was developed and validated for DBT with retrospectively collected examinations (January 2010 to December 2021) from 14 institutions in the United States and South Korea. A multicenter reader study was performed to compare the performance of 15 radiologists (seven breast specialists, eight general radiologists) in interpreting DBT examinations in 258 women (mean age, 56 years ± 13.41 [SD]), including 65 cancer cases, with and without the use of AI. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and reading time were evaluated. Results The AUC for stand-alone AI performance was 0.93 (95% CI: 0.92, 0.94). With AI, radiologists' AUC improved from 0.90 (95% CI: 0.86, 0.93) to 0.92 (95% CI: 0.88, 0.96) ( |
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| Mesh-Begriff(e) | Humans ; Female ; Breast Neoplasms/diagnostic imaging ; Middle Aged ; Mammography/methods ; Retrospective Studies ; Artificial Intelligence ; Sensitivity and Specificity ; Radiographic Image Interpretation, Computer-Assisted/methods ; Republic of Korea/epidemiology ; Deep Learning ; Adult ; Time Factors ; Algorithms ; United States ; Reproducibility of Results |
| Sprache | Englisch |
| Erscheinungsdatum | 2024-04-03 |
| Erscheinungsland | United States |
| Dokumenttyp | Journal Article ; Multicenter Study |
| ISSN | 2638-6100 |
| ISSN (online) | 2638-6100 |
| DOI | 10.1148/ryai.230318 |
| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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