Researchers are training artificial intelligence models to identify tuberculosis (TB) on chest X-rays, which may help screening and evaluation efforts in TB-prevalent areas with limited access to radiologists, according to a new study appearing online in the journal Radiology. According to the World Health Organization, TB is one of the top 10 causes of death worldwide. In 2016, approximately 10.4 million people fell ill from TB, resulting in 1.8 million deaths. TB can be identified on chest imaging, however TB-prevalent areas typically lack the radiology interpretation expertise needed to screen and diagnose the disease. "There is a tremendous interest in artificial intelligence, both inside and outside the field of medicine," said study co-author Paras Lakhani, M.D., from Thomas Jefferson University Hospital (TJUH) in Philadelphia.

Artificial intelligence models may be the new tool to help screen and evaluate efforts in tuberculosis-prevalent areas that often are plagued by limited access to radiologists. In TB-prone areas, there is a lack of trained radiologists qualified to screen and diagnose TB, which can be done using chest imaging techniques. However, the researchers used deep learning, a type of artificial intelligence that allows computers to complete tasks based on existing relationships of data. They modeled a deep convolutional neural network (DCNN) after brain structure to employ multiple hidden layers and patterns to classify images. "There is a tremendous interest in artificial intelligence, both inside and outside the field of medicine," Dr. Paras Lakhani, study co-author and assistant professor of Radiology at Thomas Jefferson University Hospital (TJUH) in Philadelphia, said in a statement.

IMAGE: (a) Posteroanterior chest radiograph shows upper lobe opacities with pathologic analysis-proven active TB. OAK BROOK, Ill. - Researchers are training artificial intelligence models to identify tuberculosis (TB) on chest X-rays, which may help screening and evaluation efforts in TB-prevalent areas with limited access to radiologists, according to a new study appearing online in the journal Radiology. According to the World Health Organization, TB is one of the top 10 causes of death worldwide. In 2016, approximately 10.4 million people fell ill from TB, resulting in 1.8 million deaths. TB can be identified on chest imaging, however TB-prevalent areas typically lack the radiology interpretation expertise needed to screen and diagnose the disease. "There is a tremendous interest in artificial intelligence, both inside and outside the field of medicine," said study co-author Paras Lakhani, M.D., from Thomas Jefferson University Hospital (TJUH) in Philadelphia.