Radiologists at Cambridge University Hospital found that more experienced radiologists are far better at detecting breast malignancy.
Top news from a Jan. 19 featured radiology search on SearchMedica: mammography
Measuring the accuracy of diagnostic imaging in symptomatic breast patients: team and individual performanceBritish Journal of Radiology | Jan 11, 2011 (Free abstract. Full text: $15)
Radiologists at Cambridge University Hospital have taken a close look at the accuracy of breast cancer diagnosis by mammography and ultrasound in their institution. Not surprisingly, they find that more experienced radiologists are far better at detecting breast malignancy. But they also reach conclusions about how to improve the performance of less experienced colleagues while assuring the best results for patients.
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.