- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
FDA Clears AI-Powered Software for Lung Nodule Detection on X-Rays
The artificial intelligence (AI)-enabled VisiRad XR reportedly demonstrated an 83 percent sensitivity rate for detecting lung nodules and masses on chest X-rays in one retrospective study.
VisiRad XR, which utilizes artificial intelligence (AI) to facilitate diagnosis of suspicious lung masses and nodules on chest X-rays, has garnered 510(k) clearance from the Food and Drug Administration (FDA).
Noting that subtle lung nodules are often overlooked on chest X-rays, IMIDEX, the manufacturer of VisiRad XR, says the software routes images with AI-detected nodules back to radiologists without the need for additional testing orders.
In a retrospective study examining the use of VisiRad XR on over 11,000 patient images, the AI software reportedly had an 83 percent sensitivity rate for identifying lung masses and nodules, according to IMIDEX. The company suggests that VisiRad XR could facilitate the additional diagnosis of 750 lung nodules or masses a year at hospitals performing 50,000 chest X-rays annually.
“Currently, only 20% of lung cancers are detected in the early stages and only 4% of patients at high risk for lung cancer are routinely screened,” noted Paul Bunn, M.D., a professor of medical oncology at the University of Colorado and former president of the American Society of Clinical Oncology (ASCO). “Increasing our detection of existing lung nodules is key to identifying it early when the patient’s likelihood of survival is markedly higher.”
Study Assesses Potential of Seven-Minute AI-Enhanced 3T MRI of the Shoulder
February 20th 2025Researchers found that the use of seven-minute threefold parallel imaging-accelerated deep learning 3T MRI had 89 percent sensitivity for supraspinatus-infraspinatus tendon tears and 93 percent sensitivity for superior labral tears.
Can CT-Based AI Provide Automated Detection of Colorectal Cancer?
February 14th 2025For the assessment of contrast-enhanced abdominopelvic CT exams, an artificial intelligence model demonstrated equivalent or better sensitivity than radiologist readers, and greater than 90 percent specificity for the diagnosis of colorectal cancer.
