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The algorithm has been validated via an unpublished study led by Dr.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.
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
