Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/73217
Title: Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population
Authors: Yanumart Malatong
Pittayarat Intasuwan
Patison Palee
Apichat Sinthubua
Pasuk Mahakkanukrauh
Keywords: Medicine
Nursing
Social Sciences
Issue Date: 1-Jan-2022
Abstract: Sex determination is a fundamental step in biological profile estimation from skeletal remains in forensic anthropology. This study proposes deep learning and morphometric technique to perform sex determination from lumbar vertebrae in a Thai population. A total of 1100 lumbar vertebrae (L1-L5) from 220 Thai individuals (110 males and 110 females) were obtained from the Forensic Osteology Research Center, Faculty of Medicine, Chiang Mai University, Thailand. In addition, two linear measurements of superior and inferior endplates from the digital caliper and image analysis were carried out for morphometric technique. Deep learning applied image classification to the superior and inferior endplates of the lumbar vertebral body. All lumbar vertebrae images are included in the dataset to increase the number of images per class. The accuracy determined the performance of each technique. The results showed the accuracies of 82.7%, 90.0%, and 92.5% for digital caliper, image analysis, and deep learning techniques, respectively. The lumbar vertebrae L1-L5 exhibit sexual dimorphism and can be used in sex estimation. Deep learning is more accurate in determining sex than the morphometric method. In addition, the subjectivity and errors in the measurement are decreased. Finally, this study presented an alternative approach to determining sex from lumbar vertebrae when the more traditionally used skeletal elements are incomplete or absent.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85127142757&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/73217
ISSN: 20421818
00258024
Appears in Collections:CMUL: Journal Articles

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