Clinical Research Directory

Effect of Physical Training in Individuals With Hypokalemic and Hyperkalemic Periodic Paralysis

This study wishes to investigate the effects of strength exercise in patients with either HypoPP or HyperPP. The investigators wishes to include participants already diagnosed with either HypoPP or HyperPP in af 24 week prospective study where the patients will be tested and asked to fill out questionnaires three times. These appointments will be schedueled at week 0, week 12 and week 24. In the time period between week 12 and week 24, the patients will have a personalized strength exercise program, which they will have to follow these 3 months. The exercise will be supervised by one or more of the investigators. We will also assess the muscle structure and function cross sectionally.

Muscle MRI Outlining of Neuromuscular Diseases Using Artificial Intelligence

Background and aim: Neuromuscular diseases encompass a range of conditions affecting muscle cells, nerves, or the interaction between the two. A common pathological feature of these conditions is the pro-gressive replacement of muscle tissue with fat, which can be visualised using magnetic reso-nance imaging (MRI). MRI-based fat quantification serves as a key biomarker for disease characterisation, progression tracking, and treatment assessment. Currently, manual segmenta-tion of MRI scans for fat quantification is very time-consuming, requiring individual muscle delineation. Therefore, an artificial intelligence (AI) model is being developed to automate the segmentation. The aim of this study is to validate this AI model and assess its possibilities and limitations. Method: The study is ongoing. Retrospective MRI scans of patients with four different muscle diseases (anoctaminopathy, Becker muscular dystrophy, facioscapulohumeral muscular dystrophy, and hypokalemic periodic paralysis) are collected and manual delineation used for training the AI-model is being performed. The intramuscular fat fraction of individual muscles of the pelvis, thigh, and calf will be analysed using the AI model. The performance of the AI model will be compared to manual segmentation. The AI will be evaluated on metrics such as segmentation accuracy and time efficiency.