Cranio : the journal of craniomandibular practice · 2026
Objective classification of masseter muscle and deep inferior tendon morphology: A comparative performance analysis of machine learning algorithms.
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Güneyli Merva, Edik Merve, Latifoğlu Fatma, Çukurluoğlu Aykağan, Çopur Şerife Arzu
Аннотация
OBJECTIVES: This study objectively classified masseter internal structure and deep inferior tendon (DIT) morphology using ultrasonography and machine learning on 229 images from 101 (DIT) and 97 (MUSCLE) patients.
METHODS: Following hybrid feature extraction (radiomics, LBP, HOG), patient-grouped, fold-internal SMOTE balancing, and LASSO-based feature selection, four algorithms were cross-validated under a leakage-free scheme.
RESULTS: All four classifiers achieved accuracy modestly but consistently above the one-third chance level for this three-class problem (47.9-54.3%), with no algorithm showing a consistent advantage. LASSO selection-frequency analysis identified a feature-specific asymmetry: a single GLSZM feature was selected in 46 of 50 folds for MUSCLE classification but never for DIT classification, alongside HOG-dominated features in both tasks.
CONCLUSIONS: This AI-based framework provides an objective approach for classifying masseter internal structure and DIT morphology, potentially reducing observer-dependent variability. These findings may underpin future studies investigating whether automated morphological classification can support clinical assessment and procedure planning.
METHODS: Following hybrid feature extraction (radiomics, LBP, HOG), patient-grouped, fold-internal SMOTE balancing, and LASSO-based feature selection, four algorithms were cross-validated under a leakage-free scheme.
RESULTS: All four classifiers achieved accuracy modestly but consistently above the one-third chance level for this three-class problem (47.9-54.3%), with no algorithm showing a consistent advantage. LASSO selection-frequency analysis identified a feature-specific asymmetry: a single GLSZM feature was selected in 46 of 50 folds for MUSCLE classification but never for DIT classification, alongside HOG-dominated features in both tasks.
CONCLUSIONS: This AI-based framework provides an objective approach for classifying masseter internal structure and DIT morphology, potentially reducing observer-dependent variability. These findings may underpin future studies investigating whether automated morphological classification can support clinical assessment and procedure planning.