Temat: dystrophy - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A pattern recognition approach to Emery-Dreifuss muscular dystrophy (EDMD) study
Autorzy:: Sokołowska, B.
Jóźwik, A.
Niebroj-Dobosz, I. M.
Hausmanowa-Petrusewicz, I.
Powiązania:: https://bibliotekanauki.pl/articles/332948.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: pattern recognition
feature selection
pair-wise linear classifier
metalloproteinases and their tissue inhibitors
Emery-Dreifuss muscular dystrophy
rozpoznawanie obrazów
wybór funkcji
metaloproteinaza
dystrofia mięśniowa
Opis:: The algorithms of pattern recognition were used for differentiation between two forms of Emery-Dreifuss muscular dystrophy (EDMD), i.e. autosomal-dominant laminopathy (AD-EDMD) and Xlinked emerynopathy (X-EDMD). A set of some matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) in serum of EDMD patients and healthy subjects were treated as features. In concluding MMPs and TIMPs levels are helpful to identifying the EDMD patients and the disease progress.
Źródło:: Journal of Medical Informatics & Technologies; 2014, 23; 165-172
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Texture-based identification of dystrophy phase. Indicating the most suitable features for therapy testing
Autorzy:: Duda, D.
Powiązania:: https://bibliotekanauki.pl/articles/333618.pdf
Data publikacji:: 2018
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: Golden Retriever Muscular Dystrophy
MRI-based tissue characterization
texture analysis
Monte Carlo feature selection
classification
dystrofia mięśniowa Duchenne'a
obrazowanie metodą rezonansu magnetycznego
analiza tekstury
klasyfikacja
Opis:: In this study, texture analysis (TA) is applied for characterization of dystrophic muscles visualized on T2-weighted Magnetic Resonance (MR) images. The study proposes a strategy for indicating the textural features that are the most appropriate for testing the therapies of Duchenne muscular dystrophy (DMD). The strategy considers that muscle texture evolves not only along with the disease progression but also with the individual’s development. First, a Monte Carlo (MC) procedure is used to assess the relative importance of each feature in identifying the phases of growth in healthy controls. The features considered as age-dependent at a given acceptance threshold are excluded from further analyses. It is assumed that their application in therapies’ evaluation may entail an incorrect assessment of dystrophy response to treatment. Next, the remaining features are used in differentiation among dystrophy phases. At this step, an MC-based feature selection is applied to find an optimal subset of features. Experiments are repeated at several acceptance thresholds for age-dependent features. Different solutions are finally compared with two classifiers: Neural Network (NN) and Support Vector Machines (SVM). The study is based on the Golden Retriever Muscular Dystrophy (GRMD) model. In total, 39 features provided by 8 TA methods (statistical, filter- and model-based) are tested.
Źródło:: Journal of Medical Informatics & Technologies; 2018, 27; 29-40
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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