|
Creation of Educational Materials on the Correct Ergonomics of Transfers in Patients with Multiple Sclerosis
Dolejšová, Šárka ; Rotbartová, Eliška (advisor) ; Rodová, Zuzana (referee)
OF BACHELOR THESIS Name, surname: Šárka Dolejšová Thesis supervisor: Mgr. Eliška Rotbartová Title: Creation of Educational Materials on the Correct Ergonomics of Transfers in Patients with Multiple Sclerosis Abstract: Multiple sclerosis (MS) is a complex neurological disease of progressive character with a diverse and usually fluctuating clinical picture. This fact is often reflected in the complexity of care, which is usually provided by informal caregivers. However, this care, especially when insufficiently informed, can lead to physical and psychological difficulties for the caregiver and the MS patient. Therefore it is important to provide assistance and offer preventive measures, including ergonomic knowledge when handling a person who is immobile. Carers can obtain this information, for example, from health professionals, other carers or educational materials, which are difficult to obtain in the Czech Republic. The main aim of the work is to create a videomanual on the correct ergonomics of transfers for people with MS. The created videomanual is primarily intended for informal caregivers. The final manual includes ergonomic recommendations for transferring people with MS and a demonstration of transferring via standing, transferring using a towel, "body- to-body" transfer and transferring...
|
|
Dual-task training in physiotherapy of people with multiple sclerosis
Kollmerová, Denisa ; Novotná, Klára (advisor) ; Kulich, Václav (referee)
Title: Dual-task training in physiotherapy of people with multiple sclerosis Abstract: This bachelor's thesis focuses on the use of dual-task elements for patients with multiple sclerosis. The thesis is based on theoretical-practical approach. The main goal is to apply therapy involving dual-task for patients with various conditions of multiple sclerosis and to describe five selected dual-task exercices for each patient with photos attached. A secondary goal is to map out dual-task difficulties in a larger sample of patients. The theoretical part is focusing on multiple sclerosis and dual-tasking. The practical part describes the implementation of physiotherapeutic intervention with dual-task elements through case studies of 3 patients with varying degrees of neurological disability, targeted to improve the subjective perception of dual-task and reduce deficits detected within kinesiological analysis, selected functional tests and questionnaires. The outcome assessment predominantly demonstrated improvements in specific tests (some even clinically significant), although in some items, patient's performance or perception remained unchanged or deteriorated. In the feedback questionnaire, patients mostly evaluated this type of intervention positively. For each patient, five selected dual-task exercices were...
|
|
Structural changes in the brain in people with multiple sclerosis and their association with clinical status
Veselá, Adéla ; Řasová, Kamila (advisor) ; Švojgrová, Andrea (referee)
Title: Structural Brain Changes in Multiple Sclerosis Patients and Their Clinical Implications The main objective: The aim of this thesis is to determine whether lesions of the corticospinal tract can affect motor skills such as balance and walking in people with MS, as measured by the clinical tests Timed Up and Go (TUG), Berg Balance Scale (BBS), and the 12-item Multiple Sclerosis Walking Scale (MSWS-12). Another aim was to determine whether a two-month facilitationn therapy would influence balance and walking assessed by TUG, BBS, and MSWS when the corticospinal tract is affected by lesions in people with MS. And whether the effect of a two-month facilitation therapy varies depending on the involvement of CST. Methods: This work is part of the study "Neuroproprioceptive "Facilitation, Inhibition" and Brain Plasticity (NEFAI)", registered under the number NCT04355663, for which paraclinical (magnetic resonance (MR) images) and clinical data (Timed Up and Go test (TUG), Berg Balance Scale (BBS), and the 12-item Multiple Sclerosis Walking Scale (MSWS) questionnaire) were obtained from people with MS between 2015 and 2017. Lesions on MR images of people with MS were delineated in the 3D slicer program as part of my bachelors thesis in 2022. For the master's thesis, these images are aligned in the...
|
|
Analysis of brain tracks using advanced diffusion methods
Daňková, Martina ; Gajdoš, Martin (referee) ; Vojtíšek, Lubomír (advisor)
The aim of this bachelor’s thesis is to analyze brain pathways using advanced diffusion magnetic resonance methods. The literature review describes the principles of diffusion-weighted imaging, methods of data collection and processing, and available software for analyzing diffusion-weighted data. The practical part of the thesis focuses on designing a functional solution for the analysis of diffusion-weighted data, which is tested on a reduced dataset containing healthy controls and patients with multiple sclerosis. A complete preprocessing, tractographic analysis, and connectome construction are performed on a reduced sample of healthy and ill patients. Additionally, an analysis of the differences between the connectomes of the healthy and the ill is conducted.
|
|
Case Study of Physiotherapy Treatment of a Patient with Diagnosis of Multiple Sclerosis
Novotný, Ondřej ; Kučerová, Ilona (advisor) ; Nováková, Tereza (referee)
Title: Case study of physiotherapeutic treatment of patient with diagnosis of multiple sclerosis Author: Ondřej Novotný Head of the thesis: Mgr. Ilona Kučerová Objectives: The aim of this thesis is to elaboratethe theoretical part of the diagnosis of multiple sclerosis. Follow-up practical processing of the case study of patient diagnosed with multiple sclerosis Methods: This work was processed based on bachelor's and specialized internships. The case study was conducted on a patient diagnosed with multiple sclerosis at Agel Říčany a. s., with these internships taking place from January 8, 2024, to February 2, 2024. Specialized methods taught at the UK FTVS were used in the bachelor's degree program scope. Initialy, the patient's medical history was taken, followed by an initial kinesiological analysis. Based on the results of the initial kinesiological analysis, a shortterm and long-term therapeutic plan were established. A total of 17 therapeutic sessions were conducted. During the final session, an exit kinesiological analysis was performed, upon which the therapy's effectiveness was evaluated. The general section involved processing theoretical information related to multiple sclerosis. Results: As a result of the therapy, the patient experienced improvement, and most of the set goals were...
|
| |
|
Segmentation of multiple sclerosis lesions using deep neural networks
Sasko, Dominik ; Myška, Vojtěch (referee) ; Kolařík, Martin (advisor)
Hlavným zámerom tejto diplomovej práce bola automatická segmentácia lézií sklerózy multiplex na snímkoch MRI. V rámci práce boli otestované najnovšie metódy segmentácie s využitím hlbokých neurónových sietí a porovnané prístupy inicializácie váh sietí pomocou preneseného učenia (transfer learning) a samoriadeného učenia (self-supervised learning). Samotný problém automatickej segmentácie lézií sklerózy multiplex je veľmi náročný, a to primárne kvôli vysokej nevyváženosti datasetu (skeny mozgov zvyčajne obsahujú len malé množstvo poškodeného tkaniva). Ďalšou výzvou je manuálna anotácia týchto lézií, nakoľko dvaja rozdielni doktori môžu označiť iné časti mozgu ako poškodené a hodnota Dice Coefficient týchto anotácií je približne 0,86. Možnosť zjednodušenia procesu anotovania lézií automatizáciou by mohlo zlepšiť výpočet množstva lézií, čo by mohlo viesť k zlepšeniu diagnostiky individuálnych pacientov. Našim cieľom bolo navrhnutie dvoch techník využívajúcich transfer learning na predtrénovanie váh, ktoré by neskôr mohli zlepšiť výsledky terajších segmentačných modelov. Teoretická časť opisuje rozdelenie umelej inteligencie, strojového učenia a hlbokých neurónových sietí a ich využitie pri segmentácii obrazu. Následne je popísaná skleróza multiplex, jej typy, symptómy, diagnostika a liečba. Praktická časť začína predspracovaním dát. Najprv boli skeny mozgu upravené na rovnaké rozlíšenie s rovnakou veľkosťou voxelu. Dôvodom tejto úpravy bolo využitie troch odlišných datasetov, v ktorých boli skeny vytvárané rozličnými prístrojmi od rôznych výrobcov. Jeden dataset taktiež obsahoval lebku, a tak bolo nutné jej odstránenie pomocou nástroju FSL pre ponechanie samotného mozgu pacienta. Využívali sme 3D skeny (FLAIR, T1 a T2 modality), ktoré boli postupne rozdelené na individuálne 2D rezy a použité na vstup neurónovej siete s enkodér-dekodér architektúrou. Dataset na trénovanie obsahoval 6720 rezov s rozlíšením 192 x 192 pixelov (po odstránení rezov, ktorých maska neobsahovala žiadnu hodnotu). Využitá loss funkcia bola Combo loss (kombinácia Dice Loss s upravenou Cross-Entropy). Prvá metóda sa zameriavala na využitie predtrénovaných váh z ImageNet datasetu na enkodér U-Net architektúry so zamknutými váhami enkodéra, resp. bez zamknutia a následného porovnania s náhodnou inicializáciou váh. V tomto prípade sme použili len FLAIR modalitu. Transfer learning dokázalo zvýšiť sledovanú metriku z hodnoty približne 0,4 na 0,6. Rozdiel medzi zamknutými a nezamknutými váhami enkodéru sa pohyboval okolo 0,02. Druhá navrhnutá technika používala self-supervised kontext enkodér s Generative Adversarial Networks (GAN) na predtrénovanie váh. Táto sieť využívala všetky tri spomenuté modality aj s prázdnymi rezmi masiek (spolu 23040 obrázkov). Úlohou GAN siete bolo dotvoriť sken mozgu, ktorý bol prekrytý čiernou maskou v tvare šachovnice. Takto naučené váhy boli následne načítané do enkodéru na aplikáciu na náš segmentačný problém. Tento experiment nevykazoval lepšie výsledky, s hodnotou DSC 0,29 a 0,09 (nezamknuté a zamknuté váhy enkodéru). Prudké zníženie metriky mohlo byť spôsobené použitím predtrénovaných váh na vzdialených problémoch (segmentácia a self-supervised kontext enkodér), ako aj zložitosť úlohy kvôli nevyváženému datasetu.
|
|
Computer analysis of medical image data
Krajčír, Róbert ; Šmirg, Ondřej (referee) ; Uher, Václav (advisor)
This work deals with medical image analysis, using variety of statisic and numeric methods implemented in Eclipse and Rapidminer environments in Java programming language. Sets of images (slices), which are used here, are the results of magnetic resonance brain examination of several subejcts. Segments in this 3D image are analyzed and some local features are computed, based on which data sets for use in training algorythms are generated. The ability of successful identification of healthy or unhealthy tissues is then practically tested using available data.
|
|
Trainable image segmentation using deep neural networks
Majtán, Martin ; Burget, Radim (referee) ; Harár, Pavol (advisor)
Diploma thesis is aimed to trainable image segmentation using deep neural networks. In the paper is explained the principle of digital image processing and image segmentation. In the paper is also explained the principle of artificial neural network, model of artificial neuron, training and activation of artificial neural network. In practical part of the paper is created an algorithm of sliding window to generate sub-images from image from magnetic rezonance. Generated sub-images are used to train, test and validate of the model of neural network. In practical part of the paper si created the model of the artificial neural network, which is used to trainable image segmentation. Model of the neural network is created using the Deeplearning4j library and it is optimized to parallel training using Spark library.
|
|
Multiple sclerosis detection
Kopuletý, Michal ; Mangová, Marie (referee) ; Uher, Václav (advisor)
This thesis is focused on detecting multiple sclerosis lesions from magnetic resonance images. Correctly retrieved lesions are very important for medical diagnosis. Detection of lesions using machine learning techniques is quite challenging because of large variability in size, shape and position of lesions in the brain. In the practical part is designed base software, which after completion will classify pixels, so that is possible to find lesions of multiple sclerosis. For classification will be used Support vector machine. Theoretical part describes multiple sclerosis, basic operations performed with biomedical images and data classification.
|