AI for Chest X-rays
qXR aids in the detection of multiple abnormal findings on a chest X-ray in less than 1 minute.
qXR can flag abnormal scans from normal and detect abnormalities in the lungs, pleura, mediastinum, bones, diaphragm, and heart. It can also aid clinicians with assessment of placement of devices and measurement of the distance from anatomical landmarks in the thorax.
Opt for an AI-assisted workflow
Integrated with multiple PACS providers globally, qXR outputs are processed and returned in under 1 minute for each scan.
Pre-read Assistance
Reduces radiology workloads
qXR is a chest X-ray screening solution built using deep learning. It classifies chest X-rays as normal or abnormal, identifies abnormal findings, and highlights them on the X-ray. The qXR algorithms have been trained and tested using a growing database (over 3.7 million) of X-rays from diverse sources.
Can be deployed on-cloud or on-premises
Output available in multiple languages
Supports scans from all major manufacturers
Clinically validated in multiple geographies
Certifications
Applications
Leading hospitals globally use qXR for pre-read assistance or as a post-read second reader.
With deep integrations available into the radiology workflow, the visual and free text outputs are pushed back to the PACS for the reporting radiologist to review. Additionally, the integration can be configured to send HL7 messages to the RIS.
ERs and ICUs use qXR to detect findings that need urgent attention on chest X-rays.
QXR aids clinicians in the detection of misplaced breathing and feeding tubes for patients who are critically ill. Designed specifically for bedside chest X-ray imaging needs, qXR can interpret supine chest X-rays and flag the ones that need to be reviewed in worklists with multiple STAT requests for injuries like pneumothorax and rib fractures.
How it works
qXR uses deep learning technology to automate the chest X-ray interpretation process. When used as a screening tool, followed by immediate bacteriological/NAAT confirmation, qXR significantly reduces time to diagnosis.
We were impressed with the seamless set-up of the algorithm allowing us to test it with very little effort and get immediate results. We also realized how easy the algorithms quantification methods make it to follow up with a patient's improvement...
Florentino Bernardo
CIO, Grupo Empresarial Angeles
Dr. Hemant Deshmukh
Dean, KEM Hospital, Head of MCGM COVID-19 taskforce
Evidence
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