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GE and R2 extend partnership
The first company to commercialize a full-field digital mammography system and the first to deliver mammography CAD have renewed their alliance. GE Medical Systems (Waukesha, WI) and R2 Technology (Sunnyvale, CA) announced Dec. 12 that R2's ImageChecker
The first company to commercialize a full-field digital mammography system and the first to deliver mammography CAD have renewed their alliance. GE Medical Systems (Waukesha, WI) and R2 Technology (Sunnyvale, CA) announced Dec. 12 that R2's ImageChecker CAD system would continue to be distributed with GE's Senographe 2000D full-field digital mammography system. More than 100 such combinations are already operating in clinical sites, according to GE, and there is room for substantial growth. More than 550 Senographe 2000D systems have been installed in the U.S., and all are upgradable to R2's CAD technology. The FDA originally cleared the ImageChecker system in 1998 for use with film-based screening mammography to assist radiologists in minimizing false-negative readings. Supplemental approvals by the FDA allowed the technology to be used in combination with the Senographe 2000D.
Could a Deep Learning Model for Mammography Improve Prediction of DCIS and Invasive Breast Cancer?
April 15th 2024Artificial intelligence (AI) assessment of mammography images may significantly enhance the prediction of invasive breast cancer and ductal carcinoma in situ (DCIS) in women with breast cancer, according to new research presented at the Society for Breast Imaging (SBI) conference.
Mammography-Based AI Abnormality Scoring May Improve Prediction of Invasive Upgrade of DCIS
April 9th 2024Emerging research suggests that an artificial intelligence (AI) score of 75 or greater for mammography abnormalities more than doubles the likelihood of invasive upgrade of ductal carcinoma in situ (DCIS) diagnosed with percutaneous biopsy.
Mammography Study: AI Improves Breast Cancer Detection and Reduces Reading Time with DBT
April 3rd 2024An emerging artificial intelligence (AI) model demonstrated more than 12 percent higher specificity and reduced image reading time by nearly six seconds in comparison to unassisted radiologist interpretation of digital breast tomosynthesis (DBT) images.
