X-Rays Combined with AI Offer Fast Diagnostic Tool in Detecting COVID-19

X-rays could be a leading-edge diagnostic tool for COVID-19 patients with the help of artificial intelligence (AI), according to researchers 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.

The findings were made by researchers at the Universidade de Fortaleza (Fortaleza - CE, Brazil) who 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. Hence, 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. Also, 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 AI 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. The researchers now 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.

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

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