The articular surfaces of the proximal segment of ulna: Morphometry and morphomechanics based on digital image analysis and concepts of fractal geometry

Al-Imam, Ahmed; Sahai, Ashok; Al-Nuaimi, Shams; Ismail, Mustafa

doi:10.1016/j.jasi.2018.04.003

Cited by 2 publications

(1 citation statement)

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“…Besides, morphometry enthusiasts can record additional parameters, including surface areas and volumetrics, to enhance the accuracy and the predictive power of statistical as well as machine learning models [47]. Overcoming the limitations of data analytics, machine learning algorithms, and the statistical package can also serve as a leverage for reliable subsequent studies in line with the methodology of the present study [48][49][50].…”

Section: K-means and Hierarchical Cluster Analysismentioning

confidence: 99%

Unification of frequentist inference and machine learning for pterygomaxillary morphometrics

et al. 2021

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Background:The base of the skull, particularly the pterygomaxillary region, has a sophisticated topography, the morphometry of which interests pathologists, maxillofacial and plastic surgeons. The aim of the study was to conduct pterygomaxillary morphometrics and test relevant hypotheses on sexual and laterality-based dimorphism, and causality relationships. Materials and methods:We handled 60 dry skulls of adult Asian males (36.7%) and females (63.3%). We calculated the prime distance D [prime] for the imaginary line from the maxillary tuberosity to the midpoint of the pterygoid process between the upper and the lower part of the pterygomaxillary fissure, as well as the parasagittal D [x-y inclin.] and coronal inclination of D [x-z inclin.] of the same line. We also took other morphometrics concerning the reference point, the maxillary tuberosity.Results: Significant sexual as well as laterality-based dimorphism and bivariate correlations existed. The univariate models could not detect any significant effect of the predictors. On the contrary, summative multivariate tests in congruence with neural networks, detected a significant effect of laterality on D [x-y inclin.] (p-value = 0.066, partial eta squared = 0.030), and the interaction of laterality and sex on D [x-z inclin.] (pvalue = 0.050, partial eta squared = 0.034). K-means clustering generated three clusters highlighting the significant classifier effect of D [prime] and its three-dimensional inclination.Conclusions: Although the predictors in our analytics had weak-to-moderate effect size underlining the existence of unknown explanatory factors, it provided novel results on the spatial inclination of the pterygoid process, and reconciled machine learning with non-Bayesian models, the application of which belongs to the realm of oral-maxillofacial surgery.

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Section: K-means and Hierarchical Cluster Analysismentioning

confidence: 99%