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.
delft-imaging-cad4tb.png
Information source: Vendor
Last updated: October 7, 2024

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
  • Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis triage (read)
  • Evaluation of C-Reactive Protein and Computer-Aided Analysis of Chest X-rays as Tuberculosis Triage Tests at Health Facilities in Lesotho and South Africa (read)

  • 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)