Samples from the dataset used in this study. (a) X-ray with PA view of a patient with COVID-19; (b) X-ray with AP view of a patient with COVID-19; (c) X-ray of a healthy patient from Dataset A; (d) X-ray of a healthy patient from Dataset B.

Samples from the dataset used in this study. (a) X-ray with PA view of a patient with COVID-19; (b) X-ray with AP view of a patient with COVID-19; (c) X-ray of a healthy patient from Dataset A; (d) X-ray of a healthy patient from Dataset B. Images courtesy of IEEE/CAA JOURNAL OF AUTOMATICA SINICA


March 29, 2021 — X-rays, first used clinically in the late 1890s, could be a leading-edge diagnostic tool for COVID-19 patients with the help of artificial intelligence, according to a team of researchers in Brazil who taught a computer program, through various machine learning methods, to detect COVID-19 in chest X-rays with 95.6 to 98.5% accuracy.

They published their results in IEEE/CAA Journal of Automatica Sinica, a joint publication of the IEEE and the Chinese Association of Automation.

The researchers have previously focused on detecting and classifying lung pathologies, such as fibrosis, emphysema and lung nodules, through medical imaging. Common symptoms presented by suspected COVID-19 infections include respiratory distress, cough and, in more aggressive cases, pneumonia - all visible via medical imaging such as computed tomography (CT) scans or X-rays.

"When the COVID-19 pandemic arose, we agreed to put our expertise to use to help deal with this new global problem," said corresponding author Victor Hugo C. de Albuquerque, a researcher in the Laboratory of Image Processing, Signals, and Applied Computing and with the Universidade de Fortaleza.

Many medical facilities have both an inadequate number of tests and lengthy processing times, Albuquerque said, so the research team focused on improving a tool that is readily available at every hospital and already frequently used in diagnosing COVID-19: X-ray devices.

"We decided to investigate if a COVID-19 infection could be automatically detected using X-ray images," Albuquerque said, noting that most X-ray images are available within minutes, compared to the days required for swab or saliva diagnostic tests.

However, the researchers found a lack of publicly available chest X-rays to train their artificial intelligence model to automatically identify the lungs of COVID-19 patients. They had just 194 COVID-19 X-rays and 194 healthy X-rays, while it usually takes thousands of images to thoroughly teach a model to detect and classify a particular target. To compensate, they took a model trained on a large dataset of other X-ray images and trained it to use the same methods to detect lungs likely infected with COVID-19. They used several different machine learning methods, two of which resulted in a 95.6% and a 98.5% accuracy rating, respectively.

"Since X-rays are very fast and cheap, they can help to triage patients in places where the health care system has collapsed or in places that are far from major centers with access to more complex technologies," Albuquerque said. "This approach to detect and classify medical images automatically can assist doctors in identifying, measuring the severity and classifying the disease."

Next, Albuquerque said, the researchers plan to continue testing their method with larger datasets as they become available, with the ultimate goal of developing a free online platform for medical image classification.

For more information: www.ieee-jas.org


Related Content

News | Digital Radiography (DR)

February 8, 2023 — Carestream Health is partnering with Robarts Research Institute to increase and demonstrate the ...

Time February 08, 2023
arrow
News | Coronavirus (COVID-19)

February 8, 2023 — New data published in the Journal of Clinical Oncology further quantify the vast lingering impact of ...

Time February 08, 2023
arrow
Videos | Information Technology

A discussion on macro trends and the future of medical imaging with Jef Williams, managing partner, Paragon Consulting ...

Time February 07, 2023
arrow
News | Radiation Therapy

February 6, 2023 — Varian, a Siemens Healthineers company, announced that it has received 510(k) clearance from the U.S ...

Time February 06, 2023
arrow
News | Radiology Business

February 2, 2023 — Five additional imaging centers across Allegheny Health Network (AHN) have been recognized by the ...

Time February 02, 2023
arrow
Feature | Radiology Business | By Melinda Taschetta-Millane

Here is a recap of what ITN viewers found most interesting during the month of January: 1. A Look at the Changes in 2023 ...

Time February 01, 2023
arrow
News | PET Imaging

January 31, 2023 — Electronic cigarette (e-cigarette) users have greater lung inflammation than cigarette smokers and ...

Time January 31, 2023
arrow
News | Ultrasound Imaging

January 31, 2023 — Esaote, a leading Italian company in the biomedical sector – in ultrasound, dedicated MRI and medical ...

Time January 31, 2023
arrow
News | Digital Pathology

January 27, 2023 — Fujifilm has completed its asset purchase of Inspirata, Inc.’s digital pathology business effective ...

Time January 27, 2023
arrow
News | Magnetic Resonance Imaging (MRI)

January 25, 2023 — On November 11th, 2022 at the Southern Hills Hospital in Las Vegas, USA, Robotic Spine Surgeon Dr ...

Time January 25, 2023
arrow
Subscribe Now