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Automatic Recognition of Ragged Red Fibers in Muscle Biopsy from Patients with Mitochondrial Disorders
Jacopo Baldacci1, Marco Calderisi1, Chiara Fiorillo2
1Kode Solutions, 56125 Pisa, Italy.
Abstract:
Mitochondrial dysfunction is considered to be a major cause of primary mitochondrial myopathy in children and adults, as reduced mitochondrial respiration and morphological changes such as ragged red fibers (RRFs) are observed in muscle biopsies. However, it is also possible to hypothesize the role of mitochondrial dysfunction in aging muscle or in secondary mitochondrial dysfunctions. The recognition of true histological patterns of mitochondrial myopathy can avoid unnecessary genetic investigations. The aim of our study was to develop and validate machine-learning methods for RRF detection in light microscopy images of skeletal muscle tissue. We used image sets of 489 color images captured from representative areas of Gomori's trichrome-stained tissue retrieved from light microscopy images at a 20× magnification. We compared the performance of random forest, gradient boosting machine, and support vector machine classifiers. Our results suggested that the advent of scanning technologies, combined with the development of machine-learning models for image classification, make neuromuscular disorders' automated diagnostic systems a concrete possibility.
Insights
Machine learning accurately detects ragged red fibers (RRFs), a key indicator of mitochondrial myopathy, in muscle biopsy images. This automated approach aids in diagnosing neuromuscular disorders and potentially avoids extensive genetic testing.
Area of Science:
- Neuromuscular Disorders
- Mitochondrial Myopathies
- Computational Pathology
Background:
- Mitochondrial dysfunction is a primary cause of myopathies, characterized by reduced respiration and ragged red fibers (RRFs) in muscle biopsies.
- Identifying histological patterns of mitochondrial myopathy is crucial to prevent unnecessary genetic investigations.
- Mitochondrial dysfunction may also play a role in muscle aging and secondary conditions.
Purpose of the Study:
- To develop and validate machine learning (ML) methods for automated detection of RRFs in skeletal muscle light microscopy images.
- To assess the efficacy of different ML classifiers for RRF identification.
Main Methods:
- Utilized a dataset of 489 color images from Gomori's trichrome-stained skeletal muscle tissue.
- Images were captured at 20× magnification using light microscopy.
- Compared the performance of Random Forest, Gradient Boosting Machine, and Support Vector Machine classifiers.
Main Results:
- Machine learning models demonstrated effectiveness in classifying RRFs in muscle tissue images.
- The study validated the potential of ML for automated RRF detection.
Conclusions:
- Automated diagnostic systems for neuromuscular disorders are feasible with advancements in scanning technology and ML.
- ML-based image classification can support the accurate diagnosis of mitochondrial myopathies.
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