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643. Vpliv fizioterapevtsko vodene vadbe v vodi in plavalnih tehnik na bolečine v križu : diplomsko delo visokošolskega strokovnega študijskega programa prve bolonjske stopnje FizioterapijaPatricija Kompan, 2024, undergraduate thesis Abstract: Uvod: Bolečine v križu spadajo med enega izmed glavnih zdravstvenih težav sodobnega časa. So ena izmed najpogostejših motenj v kostno-gibalnem sistemu in prizadenejo posameznike vseh starosti. Namen: Namen diplomskega dela je bil ugotoviti, kako uporaba vadbe v vodi in različnih plavalnih tehnik v okviru fizioterapevtski obravnavi vpliva na odpravo bolečin v križu. V sklopu naloge nas je zanimalo tudi, katere tehnike plavanja so za osebe, ki se srečujejo z bolečinami v križu, primerne. Metode: Literaturo, tako domačo kot tujo, smo pridobili iz različnih spletnih podatkovnih baz, kot so PubMed, ResearchGate in Google Scholar. Za iskanje ustrezne literature smo uporabili ključne besede v slovenskem in angleškem jeziku. Za pregled strokovne literature smo uporabili sistematični pregled literature, metasintezo in analizo. V raziskavo smo vključili deset študij. Šest študij je obravnavalo tematiko vadbe v vodi in njenih prednosti, štiri študije pa plavalne tehnike ter njihovo učinkovitost in koristnost. Rezultati: Pregled člankov kaže na pozitivne učinke vadbe v vodi in uporabe plavalnih tehnik pri osebah z bolečinami v križu. Razprava in zaključek: S pregledom raziskav ugotavljamo, da vadba v vodi omogoča učinkovito aktivacijo mišic z manjšo obremenitvijo na sklepe in hrbtenico, kar zmanjšuje tveganje za poškodbe in lajša bolečino, še posebej pri kroničnih težavah. Ob pravilni izvedbi so se vse tehnike plavanja izkazale za koristne, je pa ob nepravilni izvedbi nevarnost poškodb ali možnost poslabšanja stanja večja. Kljub kratkoročnim pozitivnim učinkom vadbe v vodi ostaja pomanjkanje raziskav o dolgoročnih učinkih. Nadaljnje raziskave so za boljše razumevanje predvsem dolgoročnih učinkov vadbe v vodi in uporabe različnih tehnik plavanja pri rehabilitacijskih postopkih oseb z bolečinami v križu nujne.
Keywords: bolečine v križu, fizioterapija, vadba v vodi, voda, plavanje, plavalne tehnike
Published in ReVIS: 09.01.2025; Views: 459; Downloads: 13
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648. Hyperbolic metric learning in machine learning algorithms for application in oncology : doctoral dissertationAlenka Trpin, 2024, doctoral dissertation Abstract: Machine learning (ML), a subset of artificial intelligence (AI), enables systems to autonomously learn and adapt without continuous human supervision, leveraging algorithms to process data, identify patterns, and refine performance through experience. This adaptive, selfteaching capability allows ML models to enhance their predictive accuracy and efficiency, making them suitable for dynamic and complex tasks. This dissertation introduces a novel approach to independent and efficient image classification, combining elements from convolutional neural networks (CNNs), hyperbolic geometry, and feature extraction. Unlike existing methods that typically rely on one of these techniques, our integrated approach merges their strengths to achieve superior performance. Additionally, we
developed derivative methods based on the original approach, which enhanced capabilities in embedding data in space. All proposed techniques were empirically evaluated on both image and numerical datasets, consistently demonstrating superior performance when compared to baseline methods. Comparative analysis confirmed that our approach achieves higher classification accuracy than traditional techniques. Given the critical role of accurate and efficient diagnostic tools in oncology, where vast amounts of data from various patient examinations need to be processed, the development of robust algorithms is essential for effective cancer diagnosis and treatment. This dissertation specifically addresses cancer image classification, focusing on the differentiation between benign and malignant lesions – an essential task for early cancer detection and treatment. Empirical results showed that embedding the data in a hyperbolic space, combined with the method for metric learning Large Margin Nearest Neighbours (LMNN) method and the use of Poincaré distance in the k-Nearest Neighbours (kNN) algorithm, yielded comparable or superior results compared to traditional classification techniques. Our findings highlight the
potential of hyperbolic embeddings and metric learning approaches to advance image classification in oncology, offering a promising direction for further research and clinical applications.
Keywords: cancer images, convolutional neural network, embedding data, hyperbolic geometry, image classification, k-nearest neighbours method
Published in ReVIS: 08.01.2025; Views: 555; Downloads: 30
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