Malatong Yanumart, Intasuwan Pittayarat, Palee Patison, Sinthubua Apichat, Mahakkanukrauh Pasuk
Program in Anatomy, Department of Anatomy, Faculty of Medicine, 26682Chiang Mai University, Chiang Mai, Thailand.
Department of Anatomy, Faculty of Medicine, 26682Chiang Mai University, Chiang Mai, Thailand.
Med Sci Law. 2023 Jan;63(1):14-21. doi: 10.1177/00258024221089073. Epub 2022 Mar 21.
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.
性别鉴定是法医人类学中根据骨骼遗骸估计生物学特征的一个基本步骤。本研究提出了深度学习和形态测量技术，用于对泰国人群的腰椎进行性别鉴定。从泰国清迈大学医学院法医骨科学研究中心获取了220名泰国个体（110名男性和110名女性）的总共1100块腰椎（L1 - L5）。此外，使用数字卡尺和图像分析对上下终板进行了两项线性测量，用于形态测量技术。深度学习将图像分类应用于腰椎椎体的上下终板。数据集中包含了所有腰椎图像，以增加每个类别的图像数量。通过准确率来确定每种技术的性能。结果显示，数字卡尺、图像分析和深度学习技术的准确率分别为82.7%、90.0%和92.5%。腰椎L1 - L5表现出性别二态性，可用于性别估计。深度学习在确定性别方面比形态测量方法更准确。此外，测量中的主观性和误差也有所降低。最后，当传统上更常用的骨骼元素不完整或缺失时，本研究提出了一种从腰椎确定性别的替代方法。
