Researchers from the Eurecat technology centre, the CVC and the University of Barcelona have developed an automated system that taps into Deep Learning technology to detect lesions caused by Covid-19 by reading computed tomography (CT) chest images.
The study, conducted by researchers Giuseppe Pezzano, Vicent Ribas, Petia Radeva and Oliver Díaz, was recently published in the journal ‘Computers in Biology and Medicine’.
The research “has enabled us to confirm the system’s efficiency as a decision support tool for healthcare professionals in screening for Covid-19 and to measure the severity, extent and evolution of SARS-CoV-2 pneumonia including over the medium and long term,” says principal investigator Giuseppe Pezzano, a researcher at Eurecat’s Digital Health Unit and the UB.
Specifically, the system works by “first segmenting the lungs from the CT image to narrow down the search area and then using the algorithm to analyse the lung area and detect the presence of Covid-19,” adds Pezzano. “If there is a positive finding, the image is processed to identify the areas affected by the disease.”
The algorithm has been tested on 79 volumes and 110 slices of CT scans in which Covid-19 infection had been detected obtained from three open-access image repositories. Average accuracy for SARS-CoV-2 lesion segmentation was about 99 percent with no false positives observed during identification.
“The accuracy of the tool developed as shown by the results of the study opens up a wide range of other applications in healthcare, a field in which Artificial Intelligence is proving to be increasingly helpful,” points out Vicent Ribas, one of the study researchers and head of the Data Analytics in Medicine research strand at Eurecat’s Digital Health Unit.
The method developed uses an innovative way of calculating the segmentation mask of medical images which has also brought outstanding results in segmenting nodules in CT scans.
Recently “papers have been published showing that Deep Learning algorithms and Computer Vision have achieved greater accuracy than medical experts in detecting cancer in mammograms and predicting strokes and heart attacks,” comments Petia Radeva, CVC researcher and Professor and head of the consolidated Computer Vision and Machine Learning Research Group at the University of Barcelona. “We wanted to be there on the frontline and so we’ve developed this technology to help doctors fight Covid-19 by providing them with high-precision algorithms to analyse medical images objectively, transparently and robustly.”
“This type of automated system is an extremely significant tool for health professionals for more robust and accurate diagnoses,” says UB Assistant Professor Oliver Díaz. “That’s because it can provide information which cannot be measured by a human being.”