Today we’re sharing new AI research that aims to improve screening for one of the top causes of death worldwide: tuberculosis (TB). TB infects 10 million people per year and disproportionately affects people in low-to-middle-income countries. Diagnosing TB early is difficult because its symptoms can mimic those of common respiratory diseases.
Cost-effective screening, specifically chest X-rays, has been identified as one way to improve the screening process. However, experts aren’t always available to interpret results. That’s why the World Health Organization (WHO) recently recommended the use of computer-aided detection (CAD) for screening and triaging.
To help catch the disease early and work toward eventually eradicating it, Google researchers developed an AI-based tool that builds on our existing work in medical imaging to identify potential TB patients for follow-up testing.
A deep learning system to detect active pulmonary tuberculosis
In a new study released this week, we found that the right deep learning system can be used to accurately identify patients who are likely to have active TB based on their chest X-ray. By using this screening tool as a preliminary step before ordering a more expensive diagnostic test, our study showed that effective AI-powered screening could save up to 80% of the cost per positive TB case detected.
Our AI-based tool was able to accurately detect active pulmonary TB cases with false-negative and false-positive detection rates that were similar to 14 radiologists. This accuracy was maintained even when examining patients who were HIV-positive, a population that is at higher risk of developing TB and is challenging to screen because their chest X-rays may differ from typical TB cases.
To make sure the model worked for patients from a wide range of races and ethnicities, we used de-identified data from nine countries to train the model and tested it on cases from five countries. These findings build on our previousresearch that showed AI can detect common issues like collapsed lungs, nodules or fractures in chest X-rays.
Applying these findings in the real world
The AI system produces a number between 0 and 1 that indicates the risk of TB. For the system to be useful in a real-world setting, there needs to be agreement about what risk level indicates that patients should be recommended for additional testing. Calibrating this threshold can be time-consuming and expensive because administrators can only come to this number after running the system on hundreds of patients, testing these patients, and analyzing the results.
Based on the performance of our model, our research suggests that any clinic could start from this default threshold and be confident that the model will perform similarly to radiologists, making it easier to deploy this technology. From there, clinics can adjust the threshold based on local needs and resources. For example, regions with fewer resources may use a higher cut-off point to reduce the number of follow-up tests needed.
The path to eradicating tuberculosis
The WHO’s “The End TB Strategy” lays out the global efforts that are underway to dramatically reduce the incidence of tuberculosis in the coming decade. Because TB can remain pervasive in communities, even if a relatively low number of people have it at a given time, more and earlier screenings are critical to reducing its prevalence.
We’ll keep contributing to these efforts — especially when it comes to research and development. Later this year, we plan to expand this work through two separate research studies with our partners, Apollo Hospitals in India and the Centre for Infectious Disease Research in Zambia (CIDRZ).
