Delft Imaging
To detect COVID-19 related abnormalities in frontal chest X-rays, Delft Imaging and Thirona developed CAD4COVID-XRay. This computer-aided detection software takes a single chest X-ray as its input, in the form of a DICOM image, and produces several outputs: a quality assessment of the input image, a heat map highlighting possible abnormal areas, a score between 0-100 indicating the percentage of visible lung area that is affected, and a score between 0 and 100 indicating the likelihood of the X-ray being abnormal and the subject on the X-ray being affected by COVID-19.
Information source: Vendor
Last updated: Oct. 1, 2023

General Information

Product name CAD4COVID-XRay
Company Delft Imaging
Subspeciality Chest
Modality X-ray
Disease targeted COVID-19
Key-features Pathology quantification, risk scores, heatmap of abnormalities
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps)

Technical Specifications

Data characteristics
Population All chest X-rays
Input Frontal chest X-rays
Input format DICOM
Output Segmentation overlay, abnormality heatmap, risk scores,
Output format DICOM, PNG, TXT
Integration Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based, Hybrid solution
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 3 - 10 seconds


Certified, Class IIa , MDD
FDA No or not yet
Intended Use Statements
Intended use (according to CE)


Market presence
On market since 06-2020
Distribution channels
Countries present (clinical, non-research use) 5
Paying clinical customers (institutes) 5
Research/test users (institutes) 60
Pricing model Pay-per-use
Based on Number of analyses


Peer reviewed papers on performance

  • COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System (read)

Non-peer reviewed papers on performance
Other relevant papers