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    "slug": "application-of-deep-learning-for-a-reliable-mri-based-diagnosis-for-progressive-supranuclear-palsy",
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    "link": "https:\/\/mediaire.ai\/de\/application-of-deep-learning-for-a-reliable-mri-based-diagnosis-for-progressive-supranuclear-palsy\/",
    "title": {
        "rendered": "Application of Deep Learning for a reliable MRI- based diagnosis for Progressive Supranuclear Palsy (PSP)"
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    "content": {
        "rendered": "<div data-elementor-type=\"wp-post\" data-elementor-id=\"3751\" class=\"elementor elementor-3751\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4f80ddb3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4f80ddb3\" 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-306b3d44\" data-id=\"306b3d44\" 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-c77e4d0 elementor-widget elementor-widget-heading\" data-id=\"c77e4d0\" 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\">Presented at<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0d710b2 elementor-widget elementor-widget-text-editor\" data-id=\"0d710b2\" 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>European Society of Radiology (2021)<\/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-d7cc196 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"d7cc196\" 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-bdcb955 elementor-widget elementor-widget-heading\" data-id=\"bdcb955\" 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-6e9e13a elementor-widget elementor-widget-text-editor\" data-id=\"6e9e13a\" 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>J. Meissner, P. Mann, H. Michaely, J. Opalka, A. Lemke<\/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-d7217bf elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"d7217bf\" 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-91d3115 elementor-widget elementor-widget-heading\" data-id=\"91d3115\" 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-14708ef3 elementor-widget elementor-widget-text-editor\" data-id=\"14708ef3\" 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><strong>Purpose<\/strong><\/p>\n<p>To quantify the performance of a new Deep Learning (DL)-based algorithm in differentiating Progressive Supranuclear Palsy (PSP) patients from healthy individuals and patients with Parkinson\u2019s disease (PD).<\/p>\n<p><strong>Materials &amp; Methods<\/strong><\/p>\n<p>Quantitative brain volumetry was carried out with our new DL-based algorithm. It was tested on 248 patients that consist of 221 healthy patients (age 61.3y\u00b110.1y, source: Parkinson\u2019s Progression Markers Initiative, PPMI), 46 patients with confirmed PD (age 64.2y\u00b18.7y, source: PPMI) and 11 patients with confirmed PSP (age 68.8y\u00b18.4y, source: internal). Images were acquired on 1.5T\/3.0T MR-scanner using 3D-T1w images. Brain volumetry and quantitative comparison against a normal model was performed for: whole brain, grey&amp;white matter, frontal, parietal, occipital, temporal lobe, hippocampus, mesencephalon, pons and all ventricles. Furthermore, the midbrain-to-pons-ratio (MtPR) was calculated in 3D. Performance was tested with respect to the algorithm&#8217;s ability to correctly identify (i) PSP against healthy patients and (ii) PSP against those with confirmed PD based on percentiles. For that, ROC was used to calculate the corresponding AUC and sensitivity\/specificity on the best performing regions.<\/p>\n<p><strong>Results<\/strong><\/p>\n<p>In the case of (i), calculations yielded highest AUC for: mesencephalon (0.944+\/-0.049) and 3D MtPR (0.896+\/-0.129) while for (ii), calculations yielded highest AUC for: mesencephalon (0.915+\/-0.084) and the occipital lobe (0.893+\/-0.085). Resulting sensitivity\/specificity calculations on the best performing region yielded values of 0.91\/0.89 for (i) and of 0.91\/0.87 for (ii).<\/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-56ec374 elementor-widget elementor-widget-image\" data-id=\"56ec374\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"800\" height=\"309\" src=\"https:\/\/mediaire.ai\/wp-content\/uploads\/2022\/10\/6-Meissner-PSP-1024x395.png\" class=\"attachment-large size-large wp-image-17904\" alt=\"\" srcset=\"https:\/\/mediaire.ai\/wp-content\/uploads\/2022\/10\/6-Meissner-PSP-1024x395.png 1024w, https:\/\/mediaire.ai\/wp-content\/uploads\/2022\/10\/6-Meissner-PSP-300x116.png 300w, https:\/\/mediaire.ai\/wp-content\/uploads\/2022\/10\/6-Meissner-PSP-768x296.png 768w, https:\/\/mediaire.ai\/wp-content\/uploads\/2022\/10\/6-Meissner-PSP-1536x592.png 1536w, https:\/\/mediaire.ai\/wp-content\/uploads\/2022\/10\/6-Meissner-PSP-18x7.png 18w, https:\/\/mediaire.ai\/wp-content\/uploads\/2022\/10\/6-Meissner-PSP.png 1910w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">ROC curves for discriminatory power between healthy and PSP patients using classical midbrain to pons ratio (MtPR) 2D\/3D on the left and mdbrain volumetry and norm value comparison on the right.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\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-dc363e3 elementor-widget elementor-widget-text-editor\" data-id=\"dc363e3\" 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><strong>Discussion<\/strong><\/p>\n<p>We successfully applied a DL approach to correctly identify PSP patients against healthy and PD patients. Our algorithm is comparable to the conventional 2D segmentation where both a reduced mesencephalon and MtPR are signs for PSP (NEUROLOGY_2005;64:2050\u20132055). In combination with a fast evaluation (&lt;4mins) our algorithm is a promising tool to aid in the diagnosis of PSP in clinical routine.<\/p>\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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