More than 10.4 million people were infected with tuberculosis in 2016, according to the Center for Disease Control and Prevention. Of those, 1.7 million died from resulting complications, many in developing regions of the world with limited access to radiology departments.

That’s why, an AI health care startup headquartered in Mumbai, developed qXR, a chest x-ray product that can identify 15 of the most common chest x-ray abnormalities. On Thursday, qXR received CE certification from the European Medical Device Directives, clearing the way for commercialization in 32 European countries.

The company plans to apply for U.S. FDA approval in the next six to 12 months.

The neural network at the heart of qXR takes just “milliseconds” to process x-rays, according to CEO Prashant Warier. “A process that can take three to four days in remote regions of the world can be done in a day,” he told VentureBeat in a phone interview.

Clinics have two choices when it comes to installation: a cloud-hosted setup in which radiologists digitize and upload scans to’s servers for analysis, or a locally hosted, on-premise solution that uses off-the-shelf hardware. “It’s mostly an automated process,” Warier said. “We’ve already deployed it in settings without … health care professionals.”

Above: A screenshot of the qXR dashboard.

Image Credit:

Training the machine learning algorithm wasn’t exactly a walk in the park, Warier said. Achieving accuracy comparable to that of human radiologists required data — lots of data. sourced anonymized x-ray scans of…