Jessica Porembka, MD, of the breast imaging division at University of Texas Southwestern Medical Center, said an ultrasound-first strategy for these lesions in DBT is cost-effective and improves efficiency.
Radiology researchers have developed and validated an automated program for tracking incidental imaging findings. The system facilitates communications between radiologists, patients and primary care providers whenever such findings turn up.
Ron Blankstein, MD, professor of radiology, Harvard Medical School, explains the use of artificial intelligence to detect heart disease in non-cardiac CT exams.
Compared to standard hip-to-waist ratio measurements and BMI, the algorithm identifies significantly more instances of metabolic syndrome and its severity in individual patients.
Jessica Porembka, MD, of the breast imaging division at University of Texas Southwestern Medical Center, said an ultrasound-first strategy for these lesions in DBT is cost-effective and improves efficiency.
Radiology researchers have developed and validated an automated program for tracking incidental imaging findings. The system facilitates communications between radiologists, patients and primary care providers whenever such findings turn up.
Ron Blankstein, MD, professor of radiology, Harvard Medical School, explains the use of artificial intelligence to detect heart disease in non-cardiac CT exams.
Compared to standard hip-to-waist ratio measurements and BMI, the algorithm identifies significantly more instances of metabolic syndrome and its severity in individual patients.
The ACR hopes these changes, including the addition of diagnostic performance feedback, will help reduce the number of patients with incidental nodules lost to follow-up each year.