{"id":20934,"date":"2025-04-24T11:03:24","date_gmt":"2025-04-24T09:03:24","guid":{"rendered":"https:\/\/mediaire.ai\/?p=20934"},"modified":"2025-05-06T09:40:02","modified_gmt":"2025-05-06T07:40:02","slug":"ai-based-assessment-of-longitudinal-multiple-sclerosis-mri-strengths-and-weaknesses-in-clinical-practice","status":"publish","type":"post","link":"https:\/\/mediaire.ai\/en\/ai-based-assessment-of-longitudinal-multiple-sclerosis-mri-strengths-and-weaknesses-in-clinical-practice\/","title":{"rendered":"AI-based assessment of longitudinal multiple sclerosis MRI: Strengths and weaknesses in clinical practice"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"20934\" class=\"elementor elementor-20934\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7c4dde7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7c4dde7\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b0151a4\" data-id=\"b0151a4\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9bb5b17 elementor-widget elementor-widget-heading\" data-id=\"9bb5b17\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Published in<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8eed059 elementor-widget elementor-widget-text-editor\" data-id=\"8eed059\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>European Journal of Radiology, Volume 183<\/p><p><!-- \/wp:paragraph --><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b01fed elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"3b01fed\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-71e8ac6 elementor-widget elementor-widget-heading\" data-id=\"71e8ac6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Authors<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b2d5a14 elementor-widget elementor-widget-text-editor\" data-id=\"b2d5a14\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Peters, S., Schmill, L., Gless, C. A., St\u00fcrner, K., Jansen, O., Seehafer, S.<\/p><p><!-- \/wp:paragraph --><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-757a4b4 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"757a4b4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e437419 elementor-widget elementor-widget-heading\" data-id=\"e437419\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8e4b497 elementor-widget elementor-widget-text-editor\" data-id=\"8e4b497\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 class=\"article-section__content en main\">Objectives<\/h2><div class=\"article-section__content en main\">In Multiple Sclerosis (MS) cerebral MRI is essential for disease and treatment monitoring. For this purpose, software solutions are available to support the radiologist with image interpretation and reporting of follow up imaging. Aim of this study was to evaluate an AI based software for longitudinal lesion detection with clinical data and to determine the influence of different MRI machines in such setting.<\/div><div>\u00a0<\/div><h3 class=\"article-section__content en main\">Methods<\/h3><div class=\"article-section__content en main\">The database of a university hospital was screened for all follow up MRI of MS patients performed in 2023. The examinations were categorized in \u201cinitial and follow up imaging at the same MRI\u201d or \u201cinitial and follow up imaging at different MRI\u201d. The examinations were analysed with the AI based software mdbrain. The results concerning new and enlarging lesions were compared with the clinical radiologic report and with a gold standard reading.<\/div><div>\u00a0<\/div><h3 class=\"article-section__content en main\">Results<\/h3><div class=\"article-section__content en main\">101 MRIs were performed at the same MRI machine and 130 at different scanners. The AI based software had a high sensitivity (1 and 0.786) and an acceptable specificity (0.74 and 0.549) concerning new or enlarging lesions in both settings. The negative predictive value was high (1 and 0.954), whereas the positive predictive value was low due to false positive new or enlarging lesions (0.444 and 0.177). The reasons for false positive lesions differed markedly in both groups.<\/div><div>\u00a0<\/div><h3 class=\"article-section__content en main\">Conclusion<\/h3><div class=\"article-section__content en main\">For the evaluation of follow up MR images of MS patients, an AI-based imaging analysis can be beneficial in clinical routine, especially due to a very high negative predictive value.<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ee42ca elementor-mobile-align-left elementor-widget elementor-widget-button\" data-id=\"7ee42ca\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;none&quot;}\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.ejradiology.com\/article\/S0720-048X(25)00027-0\/fulltext\" target=\"_blank\" download=\"poster_effectofmriacquisition.pdf\" rel=\"nofollow noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Read Full Publication<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>In Multiple Sclerosis (MS) cerebral MRI is essential for disease and treatment monitoring. For this purpose, software solutions are available to support the radiologist with image interpretation and reporting of follow up imaging. Aim of this study was to evaluate an AI based software for longitudinal lesion detection with clinical data and to determine the influence of different MRI machines in such setting.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[14],"tags":[],"class_list":["post-20934","post","type-post","status-publish","format-standard","hentry","category-publications"],"acf":[],"_links":{"self":[{"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/posts\/20934","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/comments?post=20934"}],"version-history":[{"count":7,"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/posts\/20934\/revisions"}],"predecessor-version":[{"id":20958,"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/posts\/20934\/revisions\/20958"}],"wp:attachment":[{"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/media?parent=20934"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/categories?post=20934"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mediaire.ai\/en\/wp-json\/wp\/v2\/tags?post=20934"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}