Products
Companies
News
About
About
Add your product
Contact
Newsletter
×
Subscribe to our monthly newsletter
Subscribe
Products
qXR
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.**
Information source:
Vendor
Last updated:
March 2, 2024
General Information
Technical Specifications
Regulatory
Market
Evidence
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
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)