Nav: Home

Artificial intelligence may help diagnose tuberculosis in remote areas

April 25, 2017

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. "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 Institutes of Health, the Belarus Tuberculosis Portal, and TJUH. The datasets were split into training (68.0 percent), validation (17.1 percent), and test (14.9 percent).

The cases were used to train two different DCNN models - AlexNet and GoogLeNet - which learned from TB-positive and TB-negative X-rays. The models' accuracy was tested on 150 cases that were excluded from the training and validation datasets.

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," Dr. 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 DCNN models had disagreement in 13 of the 150 test cases. For these cases, the researchers evaluated a workflow where an expert radiologist was able to interpret the images, accurately diagnosing 100 percent of the cases. This workflow, which incorporated a human in the loop, had a greater net accuracy of close to 99 percent.

"Application of deep learning to medical imaging is a relatively new field," Dr. 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."

Dr. Lakhani said that the team plans to further improve the models with mores training cases and other deep learning methods.

"We hope to prospectively apply this in a real world environment," he said. "An artificial intelligence solution using chest imaging can play a big role in tackling TB."
-end-
"Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks."

Radiology is edited by Herbert Y. Kressel, M.D., Harvard Medical School, Boston, Mass., and owned and published by the Radiological Society of North America, Inc.

RSNA is an association of over 54,600 radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Ill. (RSNA.org)

For patient-friendly information on chest imaging, visit RadiologyInfo.org.

Radiological Society of North America

Related Artificial Intelligence Articles:

Hacking the human brain -- lab-made synapses for artificial intelligence
One of the greatest challenges facing artificial intelligence development is understanding the human brain and figuring out how to mimic it.
Artificial intelligence predicts patient lifespans
A computer's ability to predict a patient's lifespan simply by looking at images of their organs is a step closer to becoming a reality, thanks to new research led by the University of Adelaide.
Building a better 'bot': Artificial intelligence helps human groups
Artificial intelligence doesn't have to be super-sophisticated to make a difference in people's lives, according to a new Yale University study.
Artificial intelligence may help diagnose tuberculosis in remote areas
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.
Biased bots: Human prejudices sneak into artificial intelligence systems
In debates over the future of artificial intelligence, many experts think of the new systems as coldly logical and objectively rational.
More Artificial Intelligence News and Artificial Intelligence Current Events

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Teaching For Better Humans
More than test scores or good grades — what do kids need to prepare them for the future? This hour, guest host Manoush Zomorodi and TED speakers explore how to help children grow into better humans, in and out of the classroom. Guests include educators Olympia Della Flora and Liz Kleinrock, psychologist Thomas Curran, and writer Jacqueline Woodson.
Now Playing: Science for the People

#534 Bacteria are Coming for Your OJ
What makes breakfast, breakfast? Well, according to every movie and TV show we've ever seen, a big glass of orange juice is basically required. But our morning grapefruit might be in danger. Why? Citrus greening, a bacteria carried by a bug, has infected 90% of the citrus groves in Florida. It's coming for your OJ. We'll talk with University of Maryland plant virologist Anne Simon about ways to stop the citrus killer, and with science writer and journalist Maryn McKenna about why throwing antibiotics at the problem is probably not the solution. Related links: A Review of the Citrus Greening...