ArticleClinical oral investigations2023
A novel method for assessment of human midpalatal sutures using CBCT-based geometric morphometrics and complexity scores.
Article in Clinical oral investigations, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors at 2 institutions in 2 countries.
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Abstract
introductionManagement of dentofacial deficiencies requires knowledge about sutural morphology and complexity. The present study assesses midpalatal sutural morphology based on human cone-beam computed tomography (CBCT) using geometric morphometrics (GMM) and complexity scores. The study is the first to apply a sutural complexity score to human CBCT datasets and demonstrates the potential such a score has to improve objectiveness and comparability when analysing the midpalatal suture. MATERIALS AND
methodsCBCTs of various age and sex groups were analysed retrospectively (n = 48). For the geometric morphometric analysis, landmark acquisition and generalised Procrustes superimposition were combined with principal component analysis to detect variability in sutural shape patterns. For complexity analysis, a windowed short-time Fourier transform with a power spectrum density (PSD) calculation was applied to resampled superimposed semi-landmarks.
resultsAccording to the GMM, younger patients exhibited comparable sutural patterns. With increasing age, the shape variation increased among the samples. The principal components did not sufficiently capture complexity patterns, so an additional methodology was applied to assess characteristics such as sutural interdigitation. According to the complexity analysis, the average PSD complexity score was 1.465 (standard deviation = 0.010). Suture complexity increased with patient age (p < 0.0001), but was not influenced by sex (p = 0.588). The intra-class correlation coefficient exceeded 0.9, indicating intra-rater reliability.
conclusionOur study demonstrated that GMM applied to human CBCTs can reveal shape variations and allow the comparison of sutural morphologies across samples. We demonstrate that complexity scores can be applied to study human sutures captured in CBCTs and complement GMM for a comprehensive sutural analysis.
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