The U.S. Food and Drug Administration (FDA) is allowing medical artificial intelligence (AI) software developer CuraCloud's new deep learning-based algorithm to be used for the detection and prioritization of incidental pneumonia findings associated with COVID-19. The findings stem from noncontrast chest CT images.
The firm's CuraRad-ICH algorithm analyzes noncontrast head CT images for the detection and prioritization of intracranial hemorrhage patients, but CuraCloud is enhancing its software to process chest CT exams. The results can be used to enable the algorithm to notify the radiology worklist that the image includes findings suggestive of pneumonia associated with COVID-19.
The enhancement is designed to improve clinical workflow through integration with PACS and enterprise worklist applications, CuraCloud said. The firm is also seeking collaborators to further develop its triage software.
![Axial images from unenhanced calcium score cardiac CT (left) and curved planar reformation images from CT angiography (right) show that higher long-term exposure to air pollution is associated with greater coronary artery calcium and more obstructive coronary artery disease (CAD). Top row: Images in a 68-year-old male patient with higher 10-year mean ambient air pollution exposure (7.9 μg/m3 for particulate matter measuring ≤2.5 μm in diameter [PM2.5] and 17.4 parts per billion [ppb] for NO2) with extensive CAD (coronary artery calcium score [CACS] >1,000 and obstructive CAD [≥70% diameter stenosis]). Bottom row: Images in a 57-year-old female patient with lower 10-year mean ambient air pollution exposure (6.3 μg/m3 for PM2.5 and 4.6 ppb for NO2) with no CAD (CACS = 0 and no obstructive stenosis).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/06/hanneman.r6SMLzkezo.png?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
