2024-04-10 ワシントン大学セントルイス校
On average, radiologists find one case of cancer (bottom images) in every 200 mammograms they evaluate. The top images show no cancer. In a recent study, researchers at Washington University School of Medicine in St. Louis and Whiterabbit.ai showed that AI assistance potentially could improve breast-cancer screening by reducing the number of false positives without missing true positives. (Image: Debbie Bennett/School of Medicine)
<関連情報>
- https://source.wustl.edu/2024/04/ai-assisted-breast-cancer-screening-may-reduce-unnecessary-testing/
- https://pubs.rsna.org/doi/10.1148/ryai.230033
マンモグラフィ検診における偽陽性所見を低減する半自律型ディープラーニングシステム A Semiautonomous Deep Learning System to Reduce False-Positive Findings in Screening Mammography
Stefano Pedemonte, Trevor Tsue , Brent Mombourquette, …
Radiology: Artificial Intelligence Published:Apr 10 2024
DOI:https://doi.org/10.1148/ryai.230033
Abstract
“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.
