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Automatic Assessment of Facial Movement in Multiple Sclerosis Patients



dc.contributor.advisorNovotný Michal
dc.contributor.authorLouis Kälble
dc.date.accessioned2023-06-12T22:52:07Z
dc.date.available2023-06-12T22:52:07Z
dc.date.issued2023-06-12
dc.identifierKOS-1240925879705
dc.identifier.urihttp://hdl.handle.net/10467/109034
dc.description.abstractMultiple Sclerosis (MS) is the most common non-traumatic, neurodegenerative disease affecting young adults worldwide with a total of 2.8 million people diagnosed. Main characteristics of the disease are the appearance of demyelinating plaque on neuronal structures. When demyelination occurs in the facial pathway, motor manifestations affecting the facial expression may arise which can include facial myokymia, hemifacial spasm or facial palsy. These all belong to the most common presentations of movement disorders in MS. Currently, there is no existing easy-to-use, objective and fully automatic tool enabling fast and reliable assessment of facial movement disruption for MS. The goal of this presented thesis is to develop a tool for the computerized, video-based assessment of facial disruption to facilitate an accurate, objective, easily applicable, and cost-effective method to evaluate facial movement in patients with MS. Forty native Czech-speakers have been recorded during a facial expressivity examination and one-minute-long video-recordings of their freely spoken monologue were used in the subsequent analysis. To analyze the disruption of facial movement, an end-to-end neural network based facial landmark detection algorithm “Face Mesh” was applied and a total of six facial movement markers have been proposed to parametrize asymmetric movement of the face. Significant differences were found in the face symmetry between MS patients with facial palsy and a healthy control group. Subsequently, a classification algorithm was trained using multinomial logistic regression that reached an AUC of 0.71. The results of this work confirmed the utility of an automated objective tool for facial disruptions in MS and present the disruption of facial movement as a possible disease biomarker. Moreover, the classification experiment emphasized the need of regional assessment in the evaluation of facial manifestations in MS.cze
dc.description.abstractMultiple Sclerosis (MS) is the most common non-traumatic, neurodegenerative disease affecting young adults worldwide with a total of 2.8 million people diagnosed. Main characteristics of the disease are the appearance of demyelinating plaque on neuronal structures. When demyelination occurs in the facial pathway, motor manifestations affecting the facial expression may arise which can include facial myokymia, hemifacial spasm or facial palsy. These all belong to the most common presentations of movement disorders in MS. Currently, there is no existing easy-to-use, objective and fully automatic tool enabling fast and reliable assessment of facial movement disruption for MS. The goal of this presented thesis is to develop a tool for the computerized, video-based assessment of facial disruption to facilitate an accurate, objective, easily applicable, and cost-effective method to evaluate facial movement in patients with MS. Forty native Czech-speakers have been recorded during a facial expressivity examination and one-minute-long video-recordings of their freely spoken monologue were used in the subsequent analysis. To analyze the disruption of facial movement, an end-to-end neural network based facial landmark detection algorithm “Face Mesh” was applied and a total of six facial movement markers have been proposed to parametrize asymmetric movement of the face. Significant differences were found in the face symmetry between MS patients with facial palsy and a healthy control group. Subsequently, a classification algorithm was trained using multinomial logistic regression that reached an AUC of 0.71. The results of this work confirmed the utility of an automated objective tool for facial disruptions in MS and present the disruption of facial movement as a possible disease biomarker. Moreover, the classification experiment emphasized the need of regional assessment in the evaluation of facial manifestations in MS.eng
dc.publisherČeské vysoké učení technické v Praze. Vypočetní a informační centrum.cze
dc.publisherCzech Technical University in Prague. Computing and Information Centre.eng
dc.rightsA university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.htmleng
dc.rightsVysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.htmlcze
dc.subjectMultiple Sclerosiscze
dc.subjectFacial Palsycze
dc.subjectFacial Landmark Detectioncze
dc.subjectDiagnostic Markerscze
dc.subjectMultiple Sclerosiseng
dc.subjectFacial Palsyeng
dc.subjectFacial Landmark Detectioneng
dc.subjectDiagnostic Markerseng
dc.titleAutomatické hodnocení obličejových pohybů u pacientů trpících roztroušenou sklerózoucze
dc.titleAutomatic Assessment of Facial Movement in Multiple Sclerosis Patientseng
dc.typediplomová prácecze
dc.typemaster thesiseng
dc.contributor.refereeMekyska Jiří
theses.degree.disciplineSignal Processingcze
theses.degree.grantorkatedra teorie obvodůcze
theses.degree.programmeMedical Electronics and Bioinformaticscze


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