{"id":118718,"date":"2021-12-01T10:07:11","date_gmt":"2021-12-01T09:07:11","guid":{"rendered":"https:\/\/eurecat.org\/?p=118718"},"modified":"2021-12-02T10:21:10","modified_gmt":"2021-12-02T09:21:10","slug":"new-system-powered-by-deep-learning-makes-it-possible-to-detect-covid-19-lesions-by-analysing-ct-chest-scans","status":"publish","type":"post","link":"https:\/\/eurecat.org\/en\/new-system-powered-by-deep-learning-makes-it-possible-to-detect-covid-19-lesions-by-analysing-ct-chest-scans\/","title":{"rendered":"New system powered by deep learning makes it possible to detect Covid-19 lesions by analysing CT chest scans"},"content":{"rendered":"<p>Researchers at the <a href=\"https:\/\/eurecat.org\/\">Eurecat<\/a> technology centre, the Computer Vision Centre (CVC) and the University of Barcelona (UB) 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.<\/p>\n<p>The <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0010482521004832?via%3Dihub\">study<\/a>, conducted by researchers Giuseppe Pezzano, Vicent Ribas, Petia Radeva and Oliver D\u00edaz, was recently published in the journal \u2018Computers in Biology and Medicine\u2019.<\/p>\n<p>The research \u201chas enabled us to confirm the system\u2019s 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,\u201d says principal investigator Giuseppe Pezzano, a researcher at Eurecat\u2019s Digital Health Unit and the UB.<\/p>\n<p>Specifically, the system works by \u201cfirst 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,\u201d adds Pezzano. \u201cIf there is a positive finding, the image is processed to identify the areas affected by the disease.\u201d<\/p>\n<p>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.<\/p>\n<p>\u201cThe 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,\u201d points out Vicent Ribas, one of the study researchers and head of the Data Analytics in Medicine research strand at Eurecat\u2019s Digital Health Unit.<\/p>\n<p>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.<\/p>\n<p>Recently \u201cpapers 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,\u201d comments Petia Radeva, Professor and head of the consolidated Computer Vision and Machine Learning Research Group at the University of Barcelona and principal investigator at the Computer Vision Centre. \u201cWe wanted to be there on the frontline and so we\u2019ve developed this technology to help doctors fight Covid-19 by providing them with high-precision algorithms to analyse medical images objectively, transparently and robustly.\u201d<\/p>\n<p>\u201cThis type of automated system is an extremely significant tool for health professionals for more robust and accurate diagnoses,\u201d says UB Assistant Professor Oliver D\u00edaz. \u201cThat\u2019s because it can provide information which cannot be measured by a human being.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers at the Eurecat technology centre, the Computer Vision Centre (CVC) and the University of Barcelona (UB) 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 D\u00edaz,  [&#8230;]<\/p>\n","protected":false},"author":12,"featured_media":118764,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[420,2284,743],"tags":[],"class_list":["post-118718","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-e-health-en","category-health","category-press-notes-en"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>New system powered by deep learning makes it possible to detect Covid-19 lesions by analysing CT chest scans - Eurecat<\/title>\n<meta name=\"description\" content=\"New system powered by deep learning makes it possible to detect Covid-19 lesions by analysing CT chest scans\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/eurecat.org\/en\/new-system-powered-by-deep-learning-makes-it-possible-to-detect-covid-19-lesions-by-analysing-ct-chest-scans\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New system powered by deep learning makes it possible to detect Covid-19 lesions by analysing CT chest scans - 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