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.
A machine learning system has come along that needs no human labeling of data for training yet matches radiologists at classifying diseases on chest X-rays—including some that the model was not specifically taught to detect.
Insured or not, few Americans are immune to medical debt. And when a budget-busting event comes, it can significantly worsen such basic social determinants of health (SDOH) as housing security and food affordability.
Briefly trained in point-of-care cardiac ultrasound, 72% of second-year medical students obtained clinical-quality views from a mannequin and 61% made the correct diagnosis in a volunteer simulated patient.
Orthopedic cone-beam CT supplier CurveBeam AI has received the FDA’s breakthrough device designation for software that computes risk of fracture in patients with osteopenia.
The nuclear imaging isotope shortage of molybdenum-99 may be over now that the sidelined reactor is restarting. ASNC's president says PET and new SPECT technologies helped cardiac imaging labs better weather the storm.
CMS has more than doubled the CCTA payment rate from $175 to $357.13. The move, expected to have a significant impact on the utilization of cardiac CT, received immediate praise from imaging specialists.
