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        <identifier>oai:figshare.com:article/34068937</identifier>
        <datestamp>2026-10-05T09:37:32Z</datestamp>
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          <dc:title>Objective classification of masseter muscle and deep inferior tendon morphology: A comparative performance analysis of machine learning algorithms</dc:title>
          <dc:creator>Merva Güneyli (25311199)</dc:creator>
          <dc:creator>Merve Edik (25311202)</dc:creator>
          <dc:creator>Fatma Latifoğlu (24411339)</dc:creator>
          <dc:creator>Aykağan Çukurluoğlu (25311205)</dc:creator>
          <dc:creator>Şerife Arzu Çopur (25311208)</dc:creator>
          <dc:subject>Space Science</dc:subject>
          <dc:subject>Environmental Sciences not elsewhere classified</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Information Systems not elsewhere classified</dc:subject>
          <dc:subject>Masseter muscle</dc:subject>
          <dc:subject>radiomics</dc:subject>
          <dc:subject>artificial intelligence</dc:subject>
          <dc:subject>ultrasonography</dc:subject>
          <dc:subject>botulinum toxin type A</dc:subject>
          <dc:description>&lt;p&gt;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.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt;</dc:description>
          <dc:date>2026-10-05T09:37:32Z</dc:date>
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          <dc:identifier>10.6084/m9.figshare.34068937.v1</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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