Artificial Intelligence May Help Diagnose Tuberculosis in Remote Areas

Chest x-ray

The relatively high accuracy of the deep learning models is exciting. The applicability for TB is important because it’s a condition for which we have treatment options. It’s a problem that can be solved.

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. “An artificial intelligence solution that could interpret radiographs for presence of TB in a cost-effective way could expand the reach of early identification and treatment in developing nations.”

Deep learning is a type of artificial intelligence that allows computers to complete tasks based on existing relationships of data. A deep convolutional neural network (DCNN), modeled after brain structure, employs multiple hidden layers and patterns to classify images.

For the study, Dr. Lakhani and his colleague, Baskaran Sundaram, M.D., obtained 1,007 X-rays of patients with and without active TB. The cases consisted of multiple chest X-ray datasets from the National…

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