qXR

Qure.ai

qXR detects abnormal chest X-rays, then identifies and localizes upto 29 common abnormalities. It also screens for tuberculosis.
Product specifications Information source: Vendor
Last updated: Aug. 26, 2021
General
Product name qXR
Company Qure.ai
Subspeciality Chest
Modality X-ray
Disease targeted Tuberculosis, Covid-19, Radiological signs seen in Lung Parenchyma, Pleura, Mediastinum, Cardiac and bones visualised in the chest X-ray
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
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
Certification
CE
Certified, Class IIa , MDD
FDA
No or not yet
Market presence
On market since 05-2018
Distribution channels Nuance, Incepto, Philips IntelliSpace, Sectra Amplifier, Blackford, GE Healthcare, Siemens
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
Peer reviewed papers on performance

  • 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