{
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    "slug": "ai-supported-detection-of-cerebral-multiple-sclerosis-lesions-decreases-radiologic-reporting-times",
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    "link": "https:\/\/mediaire.ai\/de\/ai-supported-detection-of-cerebral-multiple-sclerosis-lesions-decreases-radiologic-reporting-times\/",
    "title": {
        "rendered": "AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times"
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        "rendered": "<div data-elementor-type=\"wp-post\" data-elementor-id=\"21514\" class=\"elementor elementor-21514\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d05596a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d05596a\" 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-37ab16f\" data-id=\"37ab16f\" 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-74900b2 elementor-widget elementor-widget-heading\" data-id=\"74900b2\" 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-246178c elementor-widget elementor-widget-text-editor\" data-id=\"246178c\" 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><\/p>\n<p><em>European Journal of Radiology, Vol. 178<\/em> (2024), https:\/\/doi.org\/10.1016\/j.ejrad.2024.111638<\/p>\n<p><\/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-27e36ee elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"27e36ee\" 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-e27d31c elementor-widget elementor-widget-heading\" data-id=\"e27d31c\" 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-d1ce7e2 elementor-widget elementor-widget-text-editor\" data-id=\"d1ce7e2\" 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><\/p>\n<p>Peters, S., Kellermann, G., Watkinson, J., G\u00e4rtner, F., Huhndorf, M., St\u00fcrner, K., Jansen, O., Larsen, N.<\/p>\n<p><\/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-ee03fdb elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"ee03fdb\" 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-3553181 elementor-widget elementor-widget-heading\" data-id=\"3553181\" 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-c51c063 elementor-widget elementor-widget-text-editor\" data-id=\"c51c063\" 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<h3>Purpose<\/h3>\n<p>Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting.<\/p>\n<h3>Method<\/h3>\n<p>Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical\/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner.<\/p>\n<h3>Results<\/h3>\n<p>To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p &gt; 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p &lt; 0.001). The interrater reliabilities showed no significant differences for the determination of lesion-load (0.83 without vs. 0.8 with software support) and for the detection of new\/enlarged lesions (0.92 without vs. 0.82 with software support).<\/p>\n<h3>Conclusion<\/h3>\n<p>For the evaluation of MR images of MS patients, an AI-based support for image-interpretation can significantly decreases reporting times.<\/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-501c634 elementor-mobile-align-left elementor-widget elementor-widget-button\" data-id=\"501c634\" 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(24)00354-1\/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>",
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        "rendered": "<p>Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting. <\/p>",
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