Built with deep learning technology and trained using millions of images, our products identify and localize abnormalities on X-rays, MRI and CT scans.
Our products are currently used in 20 countries, across various radiology and healthcare facilities, including mobile vans for tuberculosis screening
Now more than ever, we are aligned to our mission of enabling high quality and affordable diagnoses across the world. We are committed to providing healthcare providers with all the support to fight the COVID-19 pandemic.
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 29 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. The qER tool is designed for triage in these settings. It detects critical abnormalities such as bleeds, fractures, mass effect and midline shift and prioritizes these critical scans on the radiology worklist so that they can be reviewed first.
The qQuant tool is used to monitor the progression of patients with traumatic brain injury. It quantifies brain stuctures and lesions, providing objective estimates of their volume over time.
Read more about Qure.ai's head CT scan products, their clinical applications, and peer-reviewed validation studies.Learn More
Qure.ai has 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 these tools 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 firstname.lastname@example.org.