Qure.ai: qXR

Problem: The widespread use of analog x-rays for the diagnosis of TB in low resource settings results in long lead times for diagnosis & initiation of treatment, and several cases going undiagnosed due to the lack of trained radiologists & clinicians at many locations. In the absence of an effective screening tool, the burden on confirmatory tests is higher leading to increased costs of testing and turnaround time and more false positives.

Solution: A smartphone based application, which uses novel AI algorithm qXR to screen patients for TB by analyzing chest x-ray of a patient, with minimal training required for a technician. A smartphone captures an analog chest x-ray and Qure.ai algorithms analyze the x-ray to allow faster screening and diagnosis of patients for TB. This is a breakthrough in improving the identification of missed cases of TB due to the non-availability of clinicians. It will also help to reduce the number of confirmatory microbiological tests required, enabling better resource utilization and also reduce the number of false positive tests. The algorithm has also been adapted to identify lung damage consistent with COVID-19, using the same platform.

Impact: qXR will enable faster, cheaper & more accurate screening of TB patients in low resource settings, increase case notifications & initiation of treatment for TB. It has the potential to bring down delay in diagnosis from an average 50 plus days to less than 7 days. It is an affordable screening test that costs only INR 100 (~$1.3) per test. qxR is also used in over 25 countries to diagnose lung damage consistent with Covid19 as a tool for effective triage at hospitals, to identify patients who need to undergo further testing and to monitor their progress This has reduced the need for radiologists to spend time on patient screening, and enabled health workers to focus on monitoring critically ill patients. The platform can also detect other lung ailments like lung cancer.