Evaluation of Palatal Rugae-Based Identification After Maxillary Expansion Using Local Morphological Patch Analysis

This study investigates the reliability of palatal rugae-based identification following maxillary expansion procedures, including Miniscrew-Assisted Rapid Palatal Expansion (MARPE) and Surgically Assisted Rapid Maxillary Expansion (SARME). Because maxillary expansion alters the morphology of the palatal vault and the spatial relationship of the palatal rugae, conventional global alignment of the anterior palate may not provide stable post-treatment identification. The study evaluates a novel three-dimensional analysis approach based on separately segmented and independently aligned palatal ruga patches, with emphasis on preserving local morphological characteristics rather than global palatal configuration. Within-subject deviations before and after treatment will be compared with between-subject deviations to determine whether individual-specific ruga morphology remains distinguishable after expansion. Machine learning-based classification models using four independently aligned ruga patches will be developed to differentiate genuine matches from impostor comparisons. The primary analysis focuses on surgical expansion groups (MARPE and SARME) to assess whether pre-expansion scans can still be matched to post-expansion scans of the same individual. Secondary analyses will compare the performance of the ruga patch model with conventional anterior palate alignment and will simulate a mixed forensic database containing both treated and untreated individuals. The findings may provide evidence for the forensic applicability of palatal rugae identification after orthopedic and surgically assisted maxillary expansion procedures.