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Adjunctive AI Powered Tool for Tuberculosis Detection Gets FDA's Breakthrough Device Designation
With reported increases in tuberculosis cases after the COVID-19 pandemic, the qSpot-TB adjunctive artificial intelligence (AI) device may facilitate improved diagnosis of the disease on chest X-rays.
The Food and Drug Administration (FDA) has granted breakthrough device designation to qSpot-TB, an artificial intelligence (AI)-powered device, which may enhance tuberlosis (TB) detection on chest X-rays.
Qure.ai, the developer of qSpot-TB, said the device localizes signs of TB on chest X-rays and provides a conclusion of whether TB is present or not.
The emergence of the qSpot-TB device is particularly timely, according to the company, given statistics from the Centers for Disease Control and Prevention (CDC) showing 8,300 reported TB cases in 2022.
The qSpot-TB device, which recently garnered a breakthrough device designation from the FDA, localizes signs of TB on chest X-rays, and provides a conclusion of whether TB is present or not, according to Qure.ai, the developer of qSpot-TB. (Image courtesy of Qure.ai)
"The increase in TB cases in USA is a reminder about the importance of collective global efforts to continue the fight against the disease until eliminated. We cannot let our guard down. Innovative technology is a crucial component for accelerated progress to successfully end TB globally,” said Professor Kenneth G. Castro, M.D., a co-director of the Emory Tuberculosis Center, and professor of global health, epidemiology, and infectious disease with the Rollins School of Public Health at Emory University.
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.
