It assigns a clinically relevant tag or tags to X-ray (for example nodule, cardiomegaly), and highlights the abnormal area. qXR trained on more than 2.4 million chest x-rays and can diagnose 18 of the most common chest abnormalities, with radiologist level accuracy in less than 30 seconds. Clinicians receive an augmented version of the X-ray, with an extra visual layer of useful information.

qXR also screens for tuberculosis (TB) and is used in public health screening programs globally.

TB is a particularly pertinent problem in Africa, with the continent contributing 24 of the 30 countries considered to have the highest burden of the disease worldwide, according to the World Health Organization (WHO). As a specialist diagnostic tool for radiologists, qXR has had a major impact on the screening of TB, allowing radiologists to automate the complex task of reading chest x-rays. The qXR solution is now part of a large prospective evaluation by the London School of Hygiene & Tropical Medicine and the Liverpool School of Tropical Medicine being conducted in Malawi.

The qER is Qure.ai’s head CT solution that can detect all critical abnormality including several kinds of intra-cerebral bleeds, their volumes and anatomy from head CT scans. The solution allows for critical cases to prioritized on a radiologists’ workflow, thereby helps in reducing turnaround times for critical cases. Interpretation of every CT scan is done in less than 3 mins.

The most recent research work compared qER to a panel of radiologists on 21,000 scans and found qER to have 95% accuracy.

Prashant Warier, Co-Founder and CEO of Qure.ai, said: “Globally, TB is the ninth leading cause of death. That makes it a major public health crisis that requires urgent action. Our solution serves as a radiology assistant, interpreting chest x-rays, detecting abnormalities and providing fast and highly accurate diagnostic result. Similarly, our head CT scan technology rapidly screens scans in under a minute to detect, localize and quantify abnormalities, as well as assess their severity enabling patient prioritization and the appropriate clinical intervention”

About Qure.ai
Qure.ai was founded in 2016. Our mission is to use artificial intelligence to make healthcare more accessible and affordable. Our core team combines deep learning expertise with clinical, scientific and regulatory knowledge. Our advisory panel consists of radiologists, other doctors and public health experts. We work with these specialists to define clinically relevant problems and design real-world solutions.

For more information: http://qure.ai

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Reena Rajan
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