
Research teams from across Belgium have joined forces to create an artificial intelligence (AI) algorithm that evaluates CT scans of patients admitted to the hospital with COVID-19.
The resulting icolung algorithm, cloud-based AI software used to quantify disease burden in COVID-19 patients on non-contrast chest CT, is intended for clinical use in quantifying lung pathology from chest CT scans. It is the result of a collaboration between software developer Icometrix, Vrije Universiteit Brussel (VUB), and other institutions.
Evaluating the type, pattern, and extent of lung pathology on chest CT can help in the assessment, triage, and follow-up of COVID-19 patients, according to the group. Triage can help alleviate the increasing burden on intensive care units and allocate resources. icolung has the potential of further decreasing workload in clinical practice by providing a fully automated assessment of the total and lobar disease burden, they said.
The algorithm can quantify total and regional lesion burden and returns a concise report and annotated images directly into the hospital PACS within 10 minutes, the researchers added. The icolung software integrates into the hospital PACS and is currently offered pro bono, they noted.












![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)






