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.
An AI startup in the neuro-oncology space has received the government’s go-ahead to market software for analyzing certain fast-growing brain tumors on MRI.
If generalizable AI models are to meaningfully contribute to precision cancer care, they’ll need to incorporate not only imaging data but also digitalized clinical notes, biomarker assays and monitor readouts.
Clinicians treating COVID-19 patients who have transplanted lungs and lower airway infection should order molecular testing in addition to, or regardless of, imaging findings.
An experimental Alzheimer’s drug therapy has slowed cognitive and functional decline by 27% versus placebo in a double-blind, randomized study of 1,795 individuals with early signs and symptoms of the disease.
Patients with autoimmune hepatitis may be better monitored across disease stages by AI-augmented multiparametric MRI than by liver biopsy, as the imaging has proven less costly and is inherently less risky due to its noninvasiveness.
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.
