- AI
- Molecular Imaging
- CT
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- Ultrasound
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- 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.”
New Survey Explores Radiologist and Neurologist Comfort Level with AI Triage for Brain MRI
January 7th 2025Survey results revealed that 71 percent of clinicians preferred adjunctive AI in facilitating triage of brain MRI scans and 58 percent were comfortable utilizing AI triage without input from radiologists.
Multicenter Study Shows Merits of AI-Powered Ultrasound Assessment for Detecting Ovarian Cancer
January 3rd 2025Adjunctive AI offered greater than seven percent increases in sensitivity, specificity, and accuracy for ultrasound detection of ovarian cancer in comparison to unassisted clinicians who lacked ultrasound expertise, according to findings from new international multicenter research.
