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Original article
Published online: 2020-12-30
Submitted: 2020-09-17
Accepted: 2020-11-24
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Reconciling artificial intelligence and non-Bayesian models for pterygomaxillary morphometrics

A. Al-Imam, I. T. Abdul-Wahaab, V. K. Konuri, A. Sahai
DOI: 10.5603/FM.a2020.0149
·
Pubmed: 33438189

open access

Ahead of Print
ORIGINAL ARTICLES
Published online: 2020-12-30
Submitted: 2020-09-17
Accepted: 2020-11-24

Abstract

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.] (p-value = 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.

Abstract

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.] (p-value = 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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Keywords

artificial intelligence, cerebral dominance, laterality, masticatory apparatus, osteology, pterygoid process, pterygopalatine fossa, stomatognathic system

About this article
Title

Reconciling artificial intelligence and non-Bayesian models for pterygomaxillary morphometrics

Journal

Folia Morphologica

Issue

Ahead of Print

Article type

Original article

Published online

2020-12-30

DOI

10.5603/FM.a2020.0149

Pubmed

33438189

Keywords

artificial intelligence
cerebral dominance
laterality
masticatory apparatus
osteology
pterygoid process
pterygopalatine fossa
stomatognathic system

Authors

A. Al-Imam
I. T. Abdul-Wahaab
V. K. Konuri
A. Sahai

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