Automated Classification of Store‐Operated Calcium Entry Activity and Disease Conditions in Murine Skeletal Muscle Images Using Machine Learning

Key Points

Classifying store-operated calcium entry activity can transform clinical approaches to muscle pathologies, improving diagnosis.
Convolutional neural networks and support vector machines effectively analyze muscle images with promising accuracy.
Assessment evaluates tissue classification automation, indicating potential for scaling in diverse clinical applications.
Exploration of larger datasets and advanced techniques, like transformer models, may enhance analysis for complex conditions.

Abstract

This study demonstrated that CNN and SVM machine learning models provide a promising approach in classifying SOCE activity in muscle images. These models offer scalable solutions for automating tissue classification, with potential to transform clinical classification in muscle pathologies. Future research can explore using larger datasets and integration of other techniques, such as transformer-based models, to improve performance in more complex muscle conditions.

Automated Classification of Store‐Operated Calcium Entry Activity and Disease Conditions in Murine Skeletal Muscle Images Using Machine Learning

Key Points

Abstract

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