A convolutional neural network (CNN) approach can accurately identify and sub-classify suspected tuberculosis (TB) on chest radiographs, according to research presented at the Society for Imaging Informatics in Medicine (SIIM) annual meeting.

While screening methods and treatment for tuberculosis have improved, it remains a leading cause of death across the globe, Jack W. Luo, MD, of McGill University in Montreal said at the conference. And in his home country, Aboriginal Canadian communities, while only 4% of the population, make up 23% of active TB cases.

“The limited financial, infrastructure and human resources available to interpret radiographs causes a key barrier to the timely diagnosis and isolation of active TB,” Luo said during the presentation. “As such, we believe that the integration of artificial intelligence in the diagnostic process can reduce the burden of interpretation and improve screening efficacy.”