Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction

<i>Background and Objectives:</i> The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthr...

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Autores principales:	Nongnut Uabundit, Arada Chaiyamoon, Sitthichai Iamsaard, Laphatrada Yurasakpong, Chanin Nantasenamat, Athikhun Suwannakhan, Nichapa Phunchago
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	pterion skull suture morphometric analysis anatomical variation machine learning Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/cef6c89dc0274d9292410fe5e2bf9598
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spelling	oai:doaj.org-article:cef6c89dc0274d9292410fe5e2bf95982021-11-25T18:19:13ZClassification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction10.3390/medicina571112821648-91441010-660Xhttps://doaj.org/article/cef6c89dc0274d9292410fe5e2bf95982021-11-01T00:00:00Zhttps://www.mdpi.com/1648-9144/57/11/1282https://doaj.org/toc/1010-660Xhttps://doaj.org/toc/1648-9144<i>Background and Objectives:</i> The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmarks. <i>Materials and methods:</i> One-hundred and twenty-four Thai dried skulls were investigated. Classification and morphometric measurement of the pterion was performed. Machine learning models were also used to interpret the morphometric findings with respect to sex and age estimation. <i>Results:</i> Spheno-parietal type was the most common type (62.1%), followed by epipteric (11.7%), fronto-temporal (5.2%) and stellate (1.2%). Complete synostosis of the pterion suture was present in 18.5% and was only present in males. While most morphometric measurements were similar between males and females, the distances from the pterion center to the mastoid process and to the external occipital protuberance were longer in males. Random forest algorithm could predict sex with 80.7% accuracy (root mean square error = 0.38) when the pterion morphometric data were provided. Correlational analysis indicated that the distances from the pterion center to the anterior aspect of the frontozygomatic suture and to the zygomatic angle were positively correlated with age, which may serve as basis for age estimation in the future. <i>Conclusions:</i> Further studies are needed to explore the use of machine learning in anatomical studies and morphometry-based sex and age estimation. Thorough understanding of the anatomy of the pterion is clinically useful when planning pterional craniotomy, particularly when the position of the pterion may change with age.Nongnut UabunditArada ChaiyamoonSitthichai IamsaardLaphatrada YurasakpongChanin NantasenamatAthikhun SuwannakhanNichapa PhunchagoMDPI AGarticlepterionskullsuturemorphometric analysisanatomical variationmachine learningMedicine (General)R5-920ENMedicina, Vol 57, Iss 1282, p 1282 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pterion skull suture morphometric analysis anatomical variation machine learning Medicine (General) R5-920
spellingShingle	pterion skull suture morphometric analysis anatomical variation machine learning Medicine (General) R5-920 Nongnut Uabundit Arada Chaiyamoon Sitthichai Iamsaard Laphatrada Yurasakpong Chanin Nantasenamat Athikhun Suwannakhan Nichapa Phunchago Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction
description	<i>Background and Objectives:</i> The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmarks. <i>Materials and methods:</i> One-hundred and twenty-four Thai dried skulls were investigated. Classification and morphometric measurement of the pterion was performed. Machine learning models were also used to interpret the morphometric findings with respect to sex and age estimation. <i>Results:</i> Spheno-parietal type was the most common type (62.1%), followed by epipteric (11.7%), fronto-temporal (5.2%) and stellate (1.2%). Complete synostosis of the pterion suture was present in 18.5% and was only present in males. While most morphometric measurements were similar between males and females, the distances from the pterion center to the mastoid process and to the external occipital protuberance were longer in males. Random forest algorithm could predict sex with 80.7% accuracy (root mean square error = 0.38) when the pterion morphometric data were provided. Correlational analysis indicated that the distances from the pterion center to the anterior aspect of the frontozygomatic suture and to the zygomatic angle were positively correlated with age, which may serve as basis for age estimation in the future. <i>Conclusions:</i> Further studies are needed to explore the use of machine learning in anatomical studies and morphometry-based sex and age estimation. Thorough understanding of the anatomy of the pterion is clinically useful when planning pterional craniotomy, particularly when the position of the pterion may change with age.
format	article
author	Nongnut Uabundit Arada Chaiyamoon Sitthichai Iamsaard Laphatrada Yurasakpong Chanin Nantasenamat Athikhun Suwannakhan Nichapa Phunchago
author_facet	Nongnut Uabundit Arada Chaiyamoon Sitthichai Iamsaard Laphatrada Yurasakpong Chanin Nantasenamat Athikhun Suwannakhan Nichapa Phunchago
author_sort	Nongnut Uabundit
title	Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction
title_short	Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction
title_full	Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction
title_fullStr	Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction
title_full_unstemmed	Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction
title_sort	classification and morphometric features of pterion in thai population with potential sex prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cef6c89dc0274d9292410fe5e2bf9598
work_keys_str_mv	AT nongnutuabundit classificationandmorphometricfeaturesofpterioninthaipopulationwithpotentialsexprediction AT aradachaiyamoon classificationandmorphometricfeaturesofpterioninthaipopulationwithpotentialsexprediction AT sitthichaiiamsaard classificationandmorphometricfeaturesofpterioninthaipopulationwithpotentialsexprediction AT laphatradayurasakpong classificationandmorphometricfeaturesofpterioninthaipopulationwithpotentialsexprediction AT chaninnantasenamat classificationandmorphometricfeaturesofpterioninthaipopulationwithpotentialsexprediction AT athikhunsuwannakhan classificationandmorphometricfeaturesofpterioninthaipopulationwithpotentialsexprediction AT nichapaphunchago classificationandmorphometricfeaturesofpterioninthaipopulationwithpotentialsexprediction
_version_	1718411347688947712

Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction

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