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Abnormalities detection in apert syndrome using hierarchical clustering algorithms

Craniosynostosis syndrome is a congenital condition occurring due to the abnormal development of the skull, leading to abnormalities in skull morphology. Apert syndrome is one of common craniosynostosis syndrome in Malaysia and this syndrome can be categorized as the severe craniofacial disorders. T...

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Main Authors: Nur Syahirah, Zulkipli, Siti Zanariah, Satari, Wan Nur Syahidah, Wan Yusoff
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2025
Subjects:
Q Science (General)
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/41424/1/Abnormalities%20detection%20in%20apert%20syndrome%20using%20hierarchical%20clustering%20algorithms.pdf
http://umpir.ump.edu.my/id/eprint/41424/
https://doi.org/10.37934/araset.44.2.135174
https://doi.org/10.37934/araset.44.2.135174
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spelling my.ump.umpir.414242024-07-31T00:43:40Z http://umpir.ump.edu.my/id/eprint/41424/ Abnormalities detection in apert syndrome using hierarchical clustering algorithms Nur Syahirah, Zulkipli Siti Zanariah, Satari Wan Nur Syahidah, Wan Yusoff Q Science (General) QA Mathematics Craniosynostosis syndrome is a congenital condition occurring due to the abnormal development of the skull, leading to abnormalities in skull morphology. Apert syndrome is one of common craniosynostosis syndrome in Malaysia and this syndrome can be categorized as the severe craniofacial disorders. The abnormalities of skull morphological in Apert syndrome patient can be identified as outliers which are investigated in this study. This study presents a skull morphological analysis based on a case study involving six paediatric patients diagnosed with Apert syndrome, alongside 22 control patients aged 0 to 12 years, all of whom underwent treatment at the University Malaya Medical Centre (UMMC). The computerized tomography scan (CTSCAN) data is provided by UMMC recorded from year 2012 until 2020 and the data is measured using MIMICS software by taking the measurement of cranial angles. The clustering-based procedures will be applied to identify the abnormalities in skull angle dataset. There are 12 skull angles and these angles are analysed using hierarchical clustering algorithms for identifying the outliers or abnormalities. The objective is to detect the abnormalities and determine the skull angles that associated with Apert syndrome (AS) in Malaysia population using clustering-based procedure. The abnormalities in Apert syndrome datasets are successfully detected by the algorithms and this study found that there are skull angles with specific location of angles are associated with Apert syndrome. This study also found that the location of skull angles for patients age 0-24 months old and >24 months old are different. The findings of this study can assist the surgical team in directing additional focus towards specific regions of the skull during the planning of interventions. This guidance has the potential to optimize surgical outcomes and reduce the risk of potential complications. Semarak Ilmu Publishing 2025-02 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/41424/1/Abnormalities%20detection%20in%20apert%20syndrome%20using%20hierarchical%20clustering%20algorithms.pdf Nur Syahirah, Zulkipli and Siti Zanariah, Satari and Wan Nur Syahidah, Wan Yusoff (2025) Abnormalities detection in apert syndrome using hierarchical clustering algorithms. Journal of Advanced Research in Applied Sciences and Engineering Technology, 44 (2). pp. 135-174. ISSN 2462-1943. (Published) https://doi.org/10.37934/araset.44.2.135174 https://doi.org/10.37934/araset.44.2.135174
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Nur Syahirah, Zulkipli
Siti Zanariah, Satari
Wan Nur Syahidah, Wan Yusoff
Abnormalities detection in apert syndrome using hierarchical clustering algorithms
description Craniosynostosis syndrome is a congenital condition occurring due to the abnormal development of the skull, leading to abnormalities in skull morphology. Apert syndrome is one of common craniosynostosis syndrome in Malaysia and this syndrome can be categorized as the severe craniofacial disorders. The abnormalities of skull morphological in Apert syndrome patient can be identified as outliers which are investigated in this study. This study presents a skull morphological analysis based on a case study involving six paediatric patients diagnosed with Apert syndrome, alongside 22 control patients aged 0 to 12 years, all of whom underwent treatment at the University Malaya Medical Centre (UMMC). The computerized tomography scan (CTSCAN) data is provided by UMMC recorded from year 2012 until 2020 and the data is measured using MIMICS software by taking the measurement of cranial angles. The clustering-based procedures will be applied to identify the abnormalities in skull angle dataset. There are 12 skull angles and these angles are analysed using hierarchical clustering algorithms for identifying the outliers or abnormalities. The objective is to detect the abnormalities and determine the skull angles that associated with Apert syndrome (AS) in Malaysia population using clustering-based procedure. The abnormalities in Apert syndrome datasets are successfully detected by the algorithms and this study found that there are skull angles with specific location of angles are associated with Apert syndrome. This study also found that the location of skull angles for patients age 0-24 months old and >24 months old are different. The findings of this study can assist the surgical team in directing additional focus towards specific regions of the skull during the planning of interventions. This guidance has the potential to optimize surgical outcomes and reduce the risk of potential complications.
format Article
author Nur Syahirah, Zulkipli
Siti Zanariah, Satari
Wan Nur Syahidah, Wan Yusoff
author_facet Nur Syahirah, Zulkipli
Siti Zanariah, Satari
Wan Nur Syahidah, Wan Yusoff
author_sort Nur Syahirah, Zulkipli
title Abnormalities detection in apert syndrome using hierarchical clustering algorithms
title_short Abnormalities detection in apert syndrome using hierarchical clustering algorithms
title_full Abnormalities detection in apert syndrome using hierarchical clustering algorithms
title_fullStr Abnormalities detection in apert syndrome using hierarchical clustering algorithms
title_full_unstemmed Abnormalities detection in apert syndrome using hierarchical clustering algorithms
title_sort abnormalities detection in apert syndrome using hierarchical clustering algorithms
publisher Semarak Ilmu Publishing
publishDate 2025
url http://umpir.ump.edu.my/id/eprint/41424/1/Abnormalities%20detection%20in%20apert%20syndrome%20using%20hierarchical%20clustering%20algorithms.pdf
http://umpir.ump.edu.my/id/eprint/41424/
https://doi.org/10.37934/araset.44.2.135174
https://doi.org/10.37934/araset.44.2.135174
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score 13.235782

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