Built with deep learning technology and trained using millions of images, our CE-certified products identify and localize abnormalities on X-rays, MRI and CT scans.
Our products are currently used in 12 countries, across various radiology and healthcare facilities, including mobile vans for tuberculosis screening
Each algorithm is validated through multiple studies versus radiologists or molecular ground truth. We often present our work at conferences and publish in peer-reviewed journals.
Each of Qure’s deep learning algorithms integrates directly with your existing workflow.
qXR detects abnormal chest X-rays, then identifies and localizes 15 common abnormalities. It also screens for tuberculosis, and is used in public health screening programs.
qXR was trained with over a million curated X-rays and radiology reports, making it hardware-agnostic and robust to variations in X-ray quality.
Read about algorithm accuracy rates and clinical validation studies.
Head CT scans are a first line diagnostic modality for patients with head injury or stroke. qER is designed for triage or diagnostic assistance in this setting. The most critical scans are prioritized on the radiology worklist so that they can be reviewed first. It detects critical abnormalities such as bleeds, fractures mass effect and midline shift, localizes them and quantifies their severity.
Read about the clinical applications, list of abnormalities detected, peer-reviewed validation studies and accuracy rates.
qQuant is a suite of quantification and progression monitoring products for CT and MRI scans. Each product features fully automated detection, quantification and 3D visualization. The level of precision and reproducibility offered by qQUANT is useful in evaluating pharmaceutical clinical trial outcomes.
Partnering with clinicians helps us identify the most relevant problems, and create real-world solutions. Much of our research is done in collaboration with hospitals, universities and research institutions. Our channel partners help us expand the reach of our deep learning algorithms, making them available to radiologists worldwide. If you’d like to collaborate with us, please reach out to email@example.com.