Vendor source

Reviewed 28 Sept 2026

Completeness Strong (72%)

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.

Supported by 21 papers

Key Features

  • Heatmap of abnormality score
  • Tuberculosis risk score

General Information

Product name

CAD4TB

Modality

X-ray

Main task

Not specified

Subspeciality

Chest

Disease targeted

Tuberculosis

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIb

Verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

Cost-effectiveness analysis of computer aided detection for tuberculosis in Ghana: A decision-analytic modeling study

PLOS One · Aug 2026

Computer-aided detection pragmatic threshold calibration in TB active case finding: A mixed-methods study in Tondo, Manila

PLOS Global Public Health · Aug 2026

Computer-aided detection for radiological disease severity classification on chest radiograph in children with intra-thoracic tuberculosis

PLOS Global Public Health · Jun 2026

Beyond accuracy: evaluating the operational feasibility and diagnostic yield of CAD4TB vs. Timika score for scalable TB screening in low-resource settings

Frontiers in Digital Health · May 2026

PET–CT benchmarked detection and 5-year progression of asymptomatic tuberculosis: a longitudinal, prospective cohort study

The Lancet Respiratory Medicine · Mar 2026

A multi-country head-to-head accuracy comparison of automated chest x-ray algorithms for tuberculosis

Annals of the American Thoracic Society · Jan 2026

Contribution of artificial intelligence to the imaging diagnosis of pediatric pulmonary tuberculosis

Revista do Instituto de Medicina Tropical de São Paulo · Jan 2026

Routine active case finding in Nigerian penitentiary institutions: comparing yields from digital X-ray with CAD4TB and WHO 4-symptom screening

BMC Infectious Diseases · Jan 2026

Improving Tuberculosis Diagnosis Through Artificial Intelligence (CAD4TB) and Stool Xpert MTB/RIF Testing: A Prospective Study From Oromia, Ethiopia

Open Forum Infectious Diseases · Dec 2025

Impact of the use of the ultra-portable digital x-ray with CAD4TB for active case finding for tuberculosis in Nigeria

Frontiers in Digital Health · Jun 2025

Performance of CAD4TB artificial intelligence technology in TB screening programmes among the adult population in South Africa and Lesotho

Journal of Clinical Tuberculosis and Other Mycobacterial Diseases · Jun 2025

Clinical evaluation of computer-aided digital x-ray detection of pulmonary tuberculosis during community-based screening or active case-finding: a case–control study

The Lancet Global Health · Feb 2025

Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria

Public Health Action · Feb 2025

Diagnostic Accuracy of Chest X-ray Computer-Aided Detection Software for Detection of Prevalent and Incident Tuberculosis in Household Contacts

Clinical Infectious Diseases · Dec 2024

Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis triage

International Journal of Infectious Diseases · Sep 2024

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

Clinical Infectious Diseases · Aug 2024

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

The Lancet Digital Health · Jul 2024

Evaluation of a population-wide, systematic screening initiative for tuberculosis on Daru island, Western Province, Papua New Guinea

BMC Public Health · Apr 2024

Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis

PLOS Digital Health · Jun 2022

Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms

The Lancet Digital Health · Aug 2021

Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system

Scientific Reports · Mar 2020

Technical Specifications

Patient population age

Not specified

Input

Posterior-anterior chest X-rays

Input format

DICOM

Output

Segmentation overlay, heatmap of abnormality, risk score

Output format

Dicom, Png, Txt

Integration

Integration in standard reading environment (PACS), Stand-alone third party application, Stand-alone webbased

Deployment

Cloud-based, Hybrid solution, Locally on dedicated hardware

Trigger for analysis

Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload

Processing time

< 10 seconds

Population

All chest X-rays

Market Presence

On market since

10-2014

AI Platforms

Resellers

Not specified

Countries present

30+

Paying clinical customers

10+

Research/test users

40+

Pricing Information

Pricing model

Not specified

Based on

Number of analyses

Source: Vendor | First published: May 2, 2024 | Last updated: Sep 28, 2026