More than 10.4 million people were infected with tuberculosis in 2016, according to the Center for Disease Control and Prevention, and 1.7 million of those people died from the resulting complications — many in developing regions with limited clinic access. That’s why Qure.ai pioneered qXR, a low-cost chest X-ray product that can identify abnormalities with AI. The company today announced that it has raised $16 million in financing, which cofounder and CEO Prashant Warier says will drive expansion and growth as Qure.ai seeks regulatory clearances around the world.
“We are proud to have taken our solutions beyond the research stage to actually impacting patient lives across more than 200 locations in 20 countries,” said Warier. “Our products ensure that life-saving treatments can be delivered to patients, even in remote locations, in a fraction of the time required for traditional scan interpretation. This funding round will allow us to further invest in R&D and to expand the reach of our solutions, accelerating our mission of delivering accessible and affordable healthcare to every human being.”
The AI model at the heart of qXR takes just “milliseconds” to process X-rays and identify up to 15 common abnormalities, including COPD, lung malignancies in high-risk populations, and certain cardiac disorders in addition to tuberculosis. To train it, Qure.ai sourced anonymized x-ray scans of tuberculosis patients from about 15 medical institutions, slowly building up a gallery of over 2.5 million images.
Clinics have two choices when it comes to installation: a cloud-hosted setup in which scans are uploaded to Qure.ai’s servers or a locally hosted, on-premise solution that uses off-the-shelf hardware. Both grant radiologists access to a workflow management platform that supports patient registration and tracking, as well as follow-up visits and X-ray screenings scheduled for those visits. From a dashboard, admins get an overview of registered patients with the size and location of their abnormalities, plus their bacteriological test results and radiology reports.
Qure.ai also offers qER, an AI-powered CT algorithm that identifies hemorrhaging in the brain and bone fractures. It notifies clinicians of critical head scans via a mobile app that displays a non-diagnostic preview and pre-populated radiology templates, most of which are informed by algorithms that detect, localize, and quantify brain pathologies including intra-cerebral bleeds and their subtypes, infarcts, mass effect, midline shift, and cranial fractures.
Sequoia India led this latest funding round, which had the participation of MassMutual Ventures Southeast Asia. Qure.ai, which was founded in 2016 and incubated by AI solutions firm Fractal Analytics, claims that over 600,000 people across 20 countries have undergone its screenings.
Qure.ai is one of several startups applying computer vision to health care challenges. Paige.ai recently raised $25 million to continue its work in cancer diagnosis with AI models trained using clinical imaging data, and Aidoc nabbed $27 million for AI-assisted head, chest, abdominal, and spinal exams. Healthy.io taps machine learning to conduct urinalysis, and Sight Diagnostics — which has raked in over $27.8 million in funding — leverages a family of algorithms to perform point-of-care complete blood count (CBC) tests with no more than a pinprick of blood.
Author: Kyle Wiggers.
Source: Venturebeat
