Software developer RadLogics has debuted the latest version of its artificial intelligence (AI)-powered COVID-19 CT algorithm.
The algorithm stems from a deep-learning process and consists of several models to detect, localize, and segment regions in the lungs infected with COVID-19. In this latest version, three different analyses can now be performed simultaneously on chest CT image scans, including the following:
- A lung's region-of-interest is cropped with lung abnormalities detected.
- The lung lobes are segmented.
- If the nodules plug-in is activated, focal ground-glass opacities (GGOs) are detected.
The measurements are key features in determining if a patient has COVID-19 or not, RadLogics said. The overall system then indicates whether the case is suspected for COVID-19 with a confidence level in percentages.
The algorithm has been validated via an unpublished study led by Dr. Hayit Greenspan from Tel Aviv University and members of the RadLogics team, in which they explored multiple descriptive lung features, including lung and infection statistics, texture, shape, and location. They used the information to train a machine learning-based classifier that distinguishes between COVID-19 and other lung abnormalities.
The study dataset included 2,191 CT cases and demonstrated a 90.8% sensitivity and 85.4% specificity.
The COVID-19 CT software is available worldwide through major OEM distribution partners including Nuance via the AI Marketplace in the U.S. market, RadLogics said.
![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)
