Nanotechnology, Deep Learning Help Detect Pediatric Tuberculosis

A technique for diagnosing tuberculosis (TB) in children takes advantage of the optical properties of gold nanoparticles to detect the disease. The technique uses an artificial intelligence (AI) algorithm to enhance the sensitivity of the test. According to the research team from the University of Washington School of Medicine and Tulane University, it is particularly challenging to diagnose TB in children. Samples of the bacteria that are markers of the disease are difficult to obtain from children, even when sputum-based testing is used. The researchers developed a way to optically detect two virulence factors for TB — the lipoarabinomannan (LAM) molecule and an associated protein called LprG. These factors are found on...