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

INTRODUCTION/AIMS: Accurate detection of pathophysiology from tissue images is critical for appropriate diagnoses and treatments of muscular dystrophies. The application of machine learning (ML) models offers a promising approach for image assessment. We compared three ML models in their ability to classify mouse skeletal muscle images based on store-operated calcium entry (SOCE) activity, as an indicator of prolonged muscle activity and/or disease. METHODS: Immunofluorescent images were collected from muscle fibers obtained from calpain-3 null mice and wildtype mice at rest or following exercise. Images were categorized with respect to SOCE activity and disease status, then split into training, validation, and testing sets. Data were then utilized by three deep learning models: Convolutional Neural Networks (CNN), EfficientNet, and Support Vector Machines (SVM). RESULTS: CNN exhibited strongest performance in accuracy (0.91) and F1 score (0.88), and SVM exhibited the highest precision (0.92). Both models achieved similar area under the receiver operating characteristic curves (0.91). Performance differences between CNN and SVM yielded a p-value of 0.19, indicating no significant differences in their ability to classify SOCE activity in muscle images. DISCUSSION: 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.