Combination Of AI With X-rays Offer Faster Diagnostic Tool F
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During a recent study, a team of researchers in Brazil through various machine learning methods taught a computer program to detect COVID-19 in chest X-rays with 95.6 to 98.5 percent accuracy. 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 CT scans or X-rays. Many medical facilities have both an inadequate number of tests and lengthy processing times, 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," researcher 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.

"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," researcher said. "This approach to detect and classify medical images automatically can assist doctors in identifying, measuring the severity and classifying the disease."

Source:
https://ieeexplore.ieee.org/document/9205687
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Mar 31, 2021Like