{"id":12645,"date":"2023-07-13T12:39:29","date_gmt":"2023-07-13T10:39:29","guid":{"rendered":"https:\/\/new.sano.science\/?post_type=research&#038;p=12645"},"modified":"2024-01-05T13:48:28","modified_gmt":"2024-01-05T12:48:28","slug":"multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/","title":{"rendered":"Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT\u00a0"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Y, Xie; B, Graf; P, Farzam; B, Baker; C, Lamoureux; A, Sitek<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">We developed and validated a research-only deep learning (DL) based automatic algorithm to detect thoracic and abdominal aortic aneurysms on contrast and non-contrast CT images and compared its performance with assessments obtained from retrospective radiology reports. The DL algorithm was developed using 556 CT scans. Manual annotations of aorta centerlines and cross-sectional aorta boundaries were created to train the algorithm. Aorta segmentation and aneurysm detection performances were evaluated on 2263 retrospective CT scans (154 thoracic and 176 abdominal aneurysms). Evaluation was performed by comparing the automatically detected aneurysm status to the aneurysm status reported in the radiology reports and the AUC was reported. In addition, a quantitative evaluation was performed to compare the automatically measured aortic diameters to manual diameters on a subset of 59 CT scans. Pearson correlation coefficient was used. For aneurysm detection, the AUC was 0.95 for thoracic aneurysm detection (95% confidence region [0.93, 0.97]) and 0.94 for abdominal aneurysm detection (95% confidence region [0.92, 0.96]). For aortic diameter measurement, the Pearson correlation coefficient was 0.973 (p&lt;0.001).<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12033\/1203332\/Multi-institutional-evaluation-of-a-deep-learning-model-for-fully\/10.1117\/12.2607877.short?SSO=1\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>In: SPIE Medical Imaging, 2022.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[17],"class_list":["post-12645","research","type-research","status-publish","hentry","research_type-publications","research_team-health-informatics-group-higs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT\u00a0 - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT\u00a0\" \/>\n<meta property=\"og:description\" content=\"In: SPIE Medical Imaging, 2022.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2024-01-05T12:48:28+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\\\/\",\"name\":\"Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT\u00a0 - Centre for Computational Personalized Medicine\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#website\"},\"datePublished\":\"2023-07-13T10:39:29+00:00\",\"dateModified\":\"2024-01-05T12:48:28+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/sano.science\\\/research\\\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/sano.science\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Research\",\"item\":\"https:\\\/\\\/sano.science\\\/research\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Publications\",\"item\":\"https:\\\/\\\/sano.science\\\/research-type\\\/publications\\\/\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT\u00a0\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/sano.science\\\/#website\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"name\":\"Centre for Computational Personalized Medicine\",\"description\":\"Sano \u2013 Centre for Computational Medicine\",\"publisher\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/sano.science\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\",\"name\":\"Sano \u2013 Centre for Computational Medicine\",\"alternateName\":\"Sano\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"contentUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"width\":700,\"height\":265,\"caption\":\"Sano \u2013 Centre for Computational Medicine\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/sano.science\\\/\",\"https:\\\/\\\/x.com\\\/sanoscience\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/sanoscience\\\/\",\"https:\\\/\\\/www.youtube.com\\\/channel\\\/UCDZ_8TcjMWUG2ZcgKKgfpwQ\",\"https:\\\/\\\/bsky.app\\\/profile\\\/sanoscience.bsky.social\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT\u00a0 - Centre for Computational Personalized Medicine","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/","og_locale":"en_US","og_type":"article","og_title":"Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT\u00a0","og_description":"In: SPIE Medical Imaging, 2022.","og_url":"https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/","og_site_name":"Centre for Computational Personalized Medicine","article_publisher":"https:\/\/www.facebook.com\/sano.science\/","article_modified_time":"2024-01-05T12:48:28+00:00","twitter_card":"summary_large_image","twitter_site":"@sanoscience","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/","url":"https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/","name":"Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT\u00a0 - Centre for Computational Personalized Medicine","isPartOf":{"@id":"https:\/\/sano.science\/#website"},"datePublished":"2023-07-13T10:39:29+00:00","dateModified":"2024-01-05T12:48:28+00:00","breadcrumb":{"@id":"https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sano.science\/research\/multi-institutional-evaluation-of-a-deep-learning-model-for-fully-automated-detection-of-aortic-aneurysms-in-contrast-and-non-contrast-ct\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/sano.science\/"},{"@type":"ListItem","position":2,"name":"Research","item":"https:\/\/sano.science\/research\/"},{"@type":"ListItem","position":3,"name":"Publications","item":"https:\/\/sano.science\/research-type\/publications\/"},{"@type":"ListItem","position":4,"name":"Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT\u00a0"}]},{"@type":"WebSite","@id":"https:\/\/sano.science\/#website","url":"https:\/\/sano.science\/","name":"Centre for Computational Personalized Medicine","description":"Sano \u2013 Centre for Computational Medicine","publisher":{"@id":"https:\/\/sano.science\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sano.science\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/sano.science\/#organization","name":"Sano \u2013 Centre for Computational Medicine","alternateName":"Sano","url":"https:\/\/sano.science\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/","url":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","contentUrl":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","width":700,"height":265,"caption":"Sano \u2013 Centre for Computational Medicine"},"image":{"@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/sano.science\/","https:\/\/x.com\/sanoscience","https:\/\/www.linkedin.com\/company\/sanoscience\/","https:\/\/www.youtube.com\/channel\/UCDZ_8TcjMWUG2ZcgKKgfpwQ","https:\/\/bsky.app\/profile\/sanoscience.bsky.social"]}]}},"acf":[],"gutenberg_blocks":[{"blockName":"custom-styles","attrs":{"styles":""}},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-CTih4j","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Y, Xie; B, Graf; P, Farzam; B, Baker; C, Lamoureux; A, Sitek<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\">Y, Xie; B, Graf; P, Farzam; B, Baker; C, Lamoureux; A, Sitek<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-DczwAK","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-HI1U6S","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">We developed and validated a research-only deep learning (DL) based automatic algorithm to detect thoracic and abdominal aortic aneurysms on contrast and non-contrast CT images and compared its performance with assessments obtained from retrospective radiology reports. The DL algorithm was developed using 556 CT scans. Manual annotations of aorta centerlines and cross-sectional aorta boundaries were created to train the algorithm. Aorta segmentation and aneurysm detection performances were evaluated on 2263 retrospective CT scans (154 thoracic and 176 abdominal aneurysms). Evaluation was performed by comparing the automatically detected aneurysm status to the aneurysm status reported in the radiology reports and the AUC was reported. In addition, a quantitative evaluation was performed to compare the automatically measured aortic diameters to manual diameters on a subset of 59 CT scans. Pearson correlation coefficient was used. For aneurysm detection, the AUC was 0.95 for thoracic aneurysm detection (95% confidence region [0.93, 0.97]) and 0.94 for abdominal aneurysm detection (95% confidence region [0.92, 0.96]). For aortic diameter measurement, the Pearson correlation coefficient was 0.973 (p&lt;0.001).<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">We developed and validated a research-only deep learning (DL) based automatic algorithm to detect thoracic and abdominal aortic aneurysms on contrast and non-contrast CT images and compared its performance with assessments obtained from retrospective radiology reports. The DL algorithm was developed using 556 CT scans. Manual annotations of aorta centerlines and cross-sectional aorta boundaries were created to train the algorithm. Aorta segmentation and aneurysm detection performances were evaluated on 2263 retrospective CT scans (154 thoracic and 176 abdominal aneurysms). Evaluation was performed by comparing the automatically detected aneurysm status to the aneurysm status reported in the radiology reports and the AUC was reported. In addition, a quantitative evaluation was performed to compare the automatically measured aortic diameters to manual diameters on a subset of 59 CT scans. Pearson correlation coefficient was used. For aneurysm detection, the AUC was 0.95 for thoracic aneurysm detection (95% confidence region [0.93, 0.97]) and 0.94 for abdominal aneurysm detection (95% confidence region [0.92, 0.96]). For aortic diameter measurement, the Pearson correlation coefficient was 0.973 (p&lt;0.001).<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-9nvv8k","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12033\/1203332\/Multi-institutional-evaluation-of-a-deep-learning-model-for-fully\/10.1117\/12.2607877.short?SSO=1","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/12645","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":4,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/12645\/revisions"}],"predecessor-version":[{"id":14709,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/12645\/revisions\/14709"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=12645"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=12645"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=12645"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}