A study presented at the 2024 Society of Nuclear Medicine and Molecular Imaging Annual Meeting found FAPI PET/CT more accurate than 18F-FDG PET/CT in staging newly diagnosed breast cancer, restaging about 20% of patients and improving clinical management.
Engineers at the University of Waterloo have developed artificial intelligence (AI) technology to predict if women with breast cancer would benefit from chemotherapy prior to surgery.
The study found false-positive results in approximately 20 percent of women who completed 10 biennial breast cancer screenings.
A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows.
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Compared with commonly used clinical risk factors, a sophisticated type of artificial intelligence called deep learning does a better job distinguishing between the mammograms of women who will later develop breast cancer and those who will not, according to a new study.
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Prediction models based on clinical characteristics and imaging findings may help reduce the false-positive rate in women with dense breasts who undergo supplemental breast cancer screening with MRI, according to a new study in the journal Radiology.
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According to an open-access article in the American Journal of Roentgenology, artificial intelligence-based computer-aided detection can be a practical addition for lowering false-positive findings when performing post-breast conserving therapy surveillance mammography.
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The funding will be used to accelerate commercial growth of ScreenPoint Medical’s Transpara AI breast care software, which uses machine learning technology and curated databases of screening mammograms to improve early diagnosis of breast cancer.
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