How artificial intelligence is helping detect tuberculosis in remote areas

TB is one of the top 10 causes of death worldwide.
By Bernie Monegain
11:41 AM
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AI detects tuberculosis

Researchers are training artificial intelligence to identify tuberculosis on chest X-rays, an initiative that could help screening and evaluation efforts in TB-prevalent areas lacking access to radiologists.

The findings are part of a study published online in the journal Radiology.

“An artificial intelligence solution that could interpret radiographs for the presence of TB in a cost-effective way could expand the reach of early identification and treatment in developing nations,” study co-author Paras Lakhani, MD, from Thomas Jefferson University Hospital in Philadelphia, wrote in the journal.

[Also: Artificial intelligence spending to surge in 2017, hit $46 billion by 2020]

For the study, Lakhani and his colleague, Baskaran Sundaram, MD, obtained 1,007 X-rays of patients with and without active TB. The cases consisted of multiple chest X-ray datasets from the National Institutes of Health, the Belarus Tuberculosis Portal, and Thomas Jefferson University Hospital.

The cases were used to train two models – AlexNet and GoogLeNet – which learned from TB-positive and TB-negative X-rays. The models’ accuracy was tested on 150 cases.

[Also: Half of hospitals to adopt artificial intelligence within 5 years]

The best performing artificial intelligence model was a combination of the AlexNet and GoogLeNet, with a net accuracy of 96 percent.

“The relatively high accuracy of the deep learning models is exciting,” Lakhani said. “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.”

The two models had disagreement in 13 of the 150 test cases. For those cases, the researchers called an expert radiologist who was able to interpret the images, accurately diagnosing 100 percent of the cases.

This workflow resulted in a net accuracy of close to 99 percent.

“Application of deep learning to medical imaging is a relatively new field,” Lakhani said. “In the past, other machine learning approaches could only get to a certain accuracy level of around 80 percent. However, with deep learning, there is potential for more accurate solutions, as this research has shown.”

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.

Twitter: @Bernie_HITN
Email the writer: bernie.monegain@himssmedia.com


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