Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/73229
Title: Comparison of sex determination using three methods applied to the greater sciatic notch of os coxae in a Thai population: Dry bone morphology, 2-dimensional photograph morphometry, and deep learning artificial neural network
Authors: Pittayarat Intasuwan
Patison Palee
Apichat Sinthubua
Pasuk Mahakkanukrauh
Keywords: Medicine
Nursing
Social Sciences
Issue Date: 1-Jan-2022
Abstract: The os coxa is commonly used for sex and age estimation with a high degree of accuracy. Our study aimed to compare the accuracy among three methods, which include a deep learning approach to increase the accuracy of sex prediction. A total sample of 250 left os coxae from a Thai population was divided into a ‘training’ set of 200 samples and a ‘test’ set of 50 samples. The age of the samples ranged from 26 to 94 years. Three methods of sex determination were assessed in this experiment: a dry bone method, an image-based method and deep learning method. The intra- and inter-observer reliabilities were also assessed in the dry bone and photo methods. Our results showed that the accuracies were 80.65%, 90.3%, and 91.95% for the dry bone, image-based, and deep learning methods, respectively. The greater sciatic notch shape was wide and symmetrical in females while narrow and asymmetrical in males. The intra- and inter-observer agreements were moderate to almost perfect level (Kappa = 0.67−0.93, ICC = 0.74−0.94). Conclusion: The image-based and deep learning methods were efficient in sex determination. However, the deep learning technique performed the best among the three methods due to its high accuracy and rapid analysis. In this study, deep learning technology was found to be a viable option for remote consultations regarding sex determination in the Thai population.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125044013&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/73229
ISSN: 00258024
Appears in Collections:CMUL: Journal Articles

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