qXR

Qure.ai
qXR detects abnormal chest X-rays, then identifies and localizes upto 29 common abnormalities. It also screens for tuberculosis.

**In the US, FDA 510(k) clearance only for facilitating confirmation of the position of the breathing tube, automated cardiothoracic ratio measurements, enhanced lung nodule detection, and triage of pneumothorax and pleural effusion.**
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Information source: Vendor
Last updated: October 7, 2024

General Information

General
Product name qXR
Company Qure.ai
Subspeciality Chest
Modality X-ray
Disease targeted Tuberculosis, Covid-19, consolidation, fibrosis, blunted CP, pleural effusion, hilar enlargement, nasogastric and endotracheal tube detection, pneumothorax, pneumo peritoneum, rib fracture, nodule, lung opacities, cavity.
Key-features Abnormality detection and localization, report generation, tuberculosis screening, worklist prioritization
Suggested use Before: flagging acute findings
During: perception aid (prompting all abnormalities/results/heatmaps), report suggestion

Technical Specifications

Data characteristics
Population All chest X-rays
Input PA/ AP view chest X-rays
Input format DICOM
Output Image annotations, free text draft radiology reports
Output format DICOM
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
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 10 - 60 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 05-2018
Distribution channels Nuance PIN, Incepto, Philips IntelliSpace, Sectra Amplifier Store, Blackford, GE Healthcare, Siemens, Calantic, deepcOS
Countries present (clinical, non-research use) 20+
Paying clinical customers (institutes) 20+
Research/test users (institutes) 10+
Pricing
Pricing model Pay-per-use, Subscription
Based on Number of installations, 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)
  • Accuracy of an artificial intelligence-enabled diagnostic assistance device in recognizing normal chest radiographs: a service evaluation (read)
  • Comparing the Output of an Artificial Intelligence Algorithm in Detecting Radiological Signs of Pulmonary Tuberculosis in Digital Chest X-Rays and Their Smartphone-Captured Photos of X-Ray Films: Retrospective Study (read)

  • Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays (read)

  • Early Detection of Heart Failure with Autonomous AI-Based Model Using Chest Radiographs: A Multicenter Study (read)

  • The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting (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)

  • Benefits of Artificial Intelligence versus Human-Reader in Chest X-ray Screening for Tuberculosis in the Philippines (read)

  • Implementing a chest X-ray artificial intelligence tool to enhance tuberculosis screening in India: Lessons learned (read)

  • Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study (read)

  • qXR v2 and v3: Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis (read)

  • Deep learning in chest radiography: Detection of findings and presence of change (read)

  • Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems (read)

  • Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India (read)

  • Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis (read)

  • Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients (read)

  • Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease (read)

  • 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

  • Can Artificial Intelligence Reliably Report Chest X-Rays?: Radiologist Validation of an Algorithm trained on 2.3 Million X-Rays (read)

  • Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest x-ray? A multiplatform evaluation of five AI products used for TB screening in a high TB-burden setting (read)
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)