Deep learning neural networks can improve the detection of tuberculosis (TB) and provide health systems with considerable cost savings, according to new findings published in Scientific Reports. The study also revealed that such AI systems can outperform radiologists.
“Deep neural networks provide opportunities for new solutions to tackle TB, which kills more people worldwide than any single infectious disease,” wrote lead author Zhi Zhen Qin of the Stop TB Partnership in Geneva, Switzerland, and colleagues. “A major reason for this high mortality is the persistent gap in detection.”
The authors examined the ability of three separate deep learning systems—CAD4TB, qXR and Lunit INSIGHT for Chest Radiography—to detect abnormalities in chest x-rays associated with TB. The retrospective study focused on more than 1,100 adult patients who presented with symptoms suggestive of TB in Nepal and Cameroon. All patients underwent a chest x-ray and a common test for diagnosing TB, the Xpert MTB/RIF assay. Chest x-rays in both Nepal and Cameroon were read twice by different radiologists. The Xpert test was used as the study’s reference standard.
Overall, Lunit and qXR both had an area under the ROC curve (AUC) of 0.94. CAD4TB, meanwhile, had an AUC of 0.92.
“We observed that all three systems performed significantly better than human radiologists and had higher AUCs than most of the current published literature on previous versions of CAD4TB,” the authors wrote. “Our results also document the first published evaluation of qXR and Lunit for detecting TB. There was no statistical difference among the AUCs of CAD4TB, Lunit, and qXR across the study sites, in pooled analysis, and when only smear negative individuals were considered.”
These systems could also help health systems save money by reducing the number of follow-up Xpert tests that occur. And any savings, the authors added, could be used by providers by help finance the purchase and implementation of AI technologies.