CAD4COVID-XRay

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

General
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
Technology
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

Regulatory

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

Market

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
Pricing model	Pay-per-use
Based on	Number of analyses

Evidence

Evidence
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