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CAD4TB
CAD4TB
Delft Imaging
To detect tuberculosis-related abnormalities in posterior anterior chest X-rays, Delft Imaging and Thirona developed CAD4TB™. 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, 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 tuberculosis.
Information source:
Vendor
Last updated:
Dec. 24, 2023
General Information
Technical Specifications
Regulatory
Market
Evidence
General Information
General
Product name
CAD4TB
Company
Delft Imaging
Subspeciality
Chest
Modality
X-ray
Disease targeted
Tuberculosis
Key-features
Tuberculosis risk score, heatmap of abnormality score
Suggested use
During: perception aid (prompting all abnormalities/results/heatmaps)
Technical Specifications
Data characteristics
Population
All chest X-rays
Input
Posterior-anterior chest X-rays
Input format
DICOM
Output
Segmentation overlay, heatmap of abnormality, risk score
Output format
Dicom, png, txt
Technology
Integration
Integration in standard reading environment (PACS), Stand-alone third party application, Stand-alone webbased
Deployment
Locally on dedicated hardware, 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 IIb
, MDR
FDA
No or not yet
Intended Use Statements
Intended use (according to CE)
Market
Market presence
On market since
10-2014
Distribution channels
Countries present (clinical, non-research use)
30+
Paying clinical customers (institutes)
10+
Research/test users (institutes)
40+
Pricing
Pricing model
Pay-per-use
Based on
Number of analyses
Evidence
Evidence
Peer reviewed papers on performance
Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software
(read)
Evaluation of a population-wide, systematic screening initiative for tuberculosis on Daru island, Western Province, Papua New Guinea
(read)
CAD4TB v6 and v7: Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis
(read)
CAD4TB v6: Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system
(read)
CAD4TB v7: Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms
(read)
Non-peer reviewed papers on performance
Other relevant papers
Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis
(read)