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qXR is used for TB screening worldwide

Automated chest X-ray reads are the future of TB screening

 

When interpreted consistently, chest X-rays are the most sensitive and cost-efficient way to screen for tuberculosis and other lung diseases. However, there are not enough qualified physicians to interpret every chest X-ray on time – leading to delays in TB diagnoses.

qXR uses deep learning technology to automate the chest X-ray interpretation process. When used as a point-of-care screening tool, followed by immediate bacteriological/NAAT confirmation, qXR significantly reduces time to diagnosis.


qXR is a CE-certified automated chest X-ray interpretation tool that screens for tuberculosis and other abnormalities

qXR is a chest X-ray screening tool that detects signs of pulmonary, hilar, and pleural tuberculosis. The artificial intelligence algorithm underlying qXR is trained to detect not only classical primary pulmonary TB, but also atypical manifestations. It can be used to simultaneously screen for COPD, lung malignancies in high-risk populations, and certain cardiac disorders.

  • Designed for use in a real-world setting, qXR is hardware-agnostic and works with X-rays of varying quality and exposure, from all X-ray machine models
  • Not only TB : The same tool can also be used to screen for other chest abnormalities
  • Zero-footprint solution, with no extra hardware required
  • Artificial intelligence algorithm trained on over 2.5 million X-rays.

Register patients, track chest X-ray and sputum status using a single platform

The AI algorithm is deployed within a complete workflow management platform that allows users to register and track patients through the process of clinical and X-ray screening, and sputum confirmation – including follow-up visits.

The admin dashboard, used by program administrators provides an overview of patients registered across sites with their X-ray screening, bacteriological test results and radiology reports. qXR is also available as a standalone cloud software that plugs into an existing workflow

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