News | Digital Radiography (DR) | January 28, 2019

Artificial Intelligence Research Receives RSNA Margulis Award

Paras Lakhani receives annual recognition of best original scientific article for research using artificial intelligence to detect tuberculosis on chest X-rays

Artificial Intelligence Research Receives RSNA Margulis Award

January 28, 2019 — The Radiological Society of North America (RSNA) presented its seventh Alexander R. Margulis Award for Scientific Excellence to Paras Lakhani, M.D., from Thomas Jefferson University Hospital (TJUH) in Philadelphia, for the article, “Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.” Lakhani was presented with the award at the 2018 RSNA annual meeting, Nov. 25-30 in Chicago.

Named for Alexander R. Margulis, M.D., a distinguished investigator and inspiring visionary in the science of radiology, this annual award recognizes the best original scientific article published in RSNA’s peer-reviewed journal Radiology.

While imaging plays a pivotal role in the diagnosis and management of tuberculosis (TB), access to radiology is often limited in the developing countries where TB is most prevalent. Hoping to bridge that gap, Lakhani and colleague Baskaran Sundaram, M.D., also from TJUH, investigated the efficacy of an automated method for detecting TB on chest radiographs. Specifically, the researchers used deep learning, a type of artificial intelligence (AI) using pre-trained deep convolutional neural networks (DCNNs), to identify TB on chest X-rays. The results of the research were promising.  

“We determined that deep learning with DCNNs can classify TB at chest radiography,” said Lakhani, lead author on the study. “This method means that radiography may facilitate screening and evaluation efforts in TB-prevalent areas with limited access to radiologists.”

This type of innovative research represents the future of radiology, according to Radiology editor David A. Bluemke, M.D., Ph.D.

“The authors evaluated a worldwide problem in public health — especially for areas with few radiologists,” Bluemke said. “Importantly, Drs. Lakhani and Sundaram validated their results by studying chest-X-rays from the United States, Belarus and China. This type of well-validated study is going to change the practice of radiology.”

The potential for improving detection of TB, one of the top 10 causes of death worldwide, was a strong motivator for the research, Lakhani said. In 2016, approximately 10.4 million people fell ill from TB, resulting in 1.8 million deaths, according to the World Health Organization (WHO).

“An automated solution — or proof that an automated solution could work — could change the landscape of this disease, particularly in developing countries like Sub-Saharan Africa,” Lakhani said. “A great priority of WHO is ending TB.”

For the study, Lakhani and Sundaram obtained 1,007 X-rays of patients with and without active TB, consisting 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 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 AI model was a combination of the AlexNet and GoogLeNet, with a net accuracy of 96 percent.

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, incorporating a human into the loop, had a greater net accuracy of close to 99 percent.

The DCNNs were not trained to distinguish potential mimics of pulmonary TB, such as lung cancer, bacterial pneumonia or tropical diseases, according to Lakhani.

“The goal of such algorithms is to differentiate normal from abnormal chest X-rays with respect to TB evaluation,” Lakhani said. “Those flagged as abnormal with characteristics of pulmonary TB should be followed by bacteriologic confirmation, as suggested by screening workflows presented by WHO. The goal in these workflows is cost savings, as the cost of digital radiography has substantially lowered in the past decade.”

Lakhani, who completed his fellowship training in nuclear medicine and positron emission tomography/computed tomography (PET/CT), has been a radiologist since 2011, primarily specializing in cardiac radiology at TJUH. He said, along with being a tremendous honor, the Margulis Award provides momentum for his plans to further improve the models with more training cases and other deep learning methods.

“This award was so unexpected, and I am truly honored,” Lakhani said. “Artificial intelligence is a hot area of research, and I have been focusing on this area for about two years. I don’t plan to change direction any time soon.”

While this was a retrospective study based on datasets available at the time of the study, Lakhani hopes to broaden the study by investigating the use of DCNNs in a clinical practice for evaluating TB.

“With deep learning, the more data you have, the better you do,” he said. “There is a plenty of data internationally to develop more robust algorithms, and the future is exciting for this type of research.”

For more information: www.pubs.rsna.org/journal/radiology

 

Reference

1. Lakhani P., Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology, April 24, 2017. https://doi.org/10.1148/radiol.2017162326

Related Content

Sponsored Content | Videos | Artificial Intelligence | February 21, 2020
In Artificial Intelligence at RSNA 2019, ITN Contributing Editor Greg Freiherr offers an overview of artificial intel
Sponsored Content | Videos | Enterprise Imaging | February 21, 2020
In Enterprise Imaging at RSNA 2019, ITN Contributing Editor Greg Freiherr offers an overvie
An example of the MRI scans showing long-term and short-term survival indications. #MRI

An example of the MRI scans showing long-term and short-term survival indications. Image courtesy of Case Western Reserve University

News | Magnetic Resonance Imaging (MRI) | February 21, 2020
February 21, 2020 — ...
A cutting-edge magnet resonance imaging (MRI) technique to detect iron deposits in different brain regions can track declines in thinking, memory and movement in people with Parkinson's disease #Parkinsons #MRI

Summary steps of the processing pipeline for QSM reconstruction (phase pre-processing and map estimation) and whole brain/regional analysis. ANTs, advanced normalisation tools; MP-RAGE, magnetisation-prepared, 3D, rapid, gradient-echo; MSDI, multi-scale dipole inversion; QSM, quantitative susceptibility mapping; ROI, region of interest; SWI, susceptibility weighted imaging.

News | Magnetic Resonance Imaging (MRI) | February 21, 2020
February 21, 2020 — A cutting-edge...
Chest CT imaging of patient. #coronavirus #nCoV2019 #2019nCoV #COVID19

Examples of typical chest CT findings compatible with COVID-19 pneumonia in patients with epidemiological and clinical presentation suspicious for COVID-19 infection. This image is part of the original research, Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR, published Feb. 19, 2020, in Radiology Online.

News | Computed Tomography (CT) | February 19, 2020
February 19, 2020 — In new research
Sponsored Content | Videos | Enterprise Imaging | February 19, 2020
Bill Lacy, vice president, Medical Informatics at FUJIFILM Medic...
Sponsored Content | Videos | Flat Panel Displays | February 19, 2020
EIZO medical monitors were showcased recently at RSN...
Recognized as the “Pulitzer Prize of the business press,” the Jesse H. Neal Award finalists are selected for exhibiting journalistic enterprise, service to the industry and editorial craftsmanship
News | Radiology Business | February 19, 2020
February 19, 2020 — Connectiv, a division of The Software and Information Industry Association (SIIA), has announced
Arizona State University researchers (in collaboration with Banner MD Anderson Cancer Center) have discovered a biocompatible cost-effective hydrogel that can be used to monitor therapeutic doses of ionizing radiation by becoming more pink with increasing radiation exposure

Arizona State University researchers (in collaboration with Banner MD Anderson Cancer Center) have discovered a biocompatible cost-effective hydrogel that can be used to monitor therapeutic doses of ionizing radiation by becoming more pink with increasing radiation exposure. This picture shows a circle of hydrogel that was irradiated on the left half, which is slightly pink; whereas the right half of the gel is not irradiated and remains colorless.

News | Radiation Therapy | February 18, 2020
February 18, 2020 — More than half of all cancer patients undergo radiation therapy and the dose is critical.
The Caption Guidance software uses artificial intelligence to guide users to get optimal cardiac ultrasound images in a point of care ultrasound (POCUS) setting.

The Caption Guidance software uses artificial intelligence to guide users to get optimal cardiac ultrasound images in a point of care ultrasound (POCUS) setting.

News | Artificial Intelligence | February 13, 2020
February 13, 2020 — The U.S.