Anatomy and Cell Biology

  • 제56권1호
  • /
  • Pages.86-93
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  • 2023
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  • 2093-3665(pISSN)
  • /
  • 2093-3673(eISSN)
대한해부학회 (Korean Association of Anatomists)
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Convolutional neural network of age-related trends digital radiographs of medial clavicle in a Thai population: a preliminary study

  • Phisamon Kengkard (Faculty of Medicine, Chiang Mai University) ;
  • Jirachaya Choovuthayakorn (Faculty of Medicine, Chiang Mai University) ;
  • Chollada Mahakkanukrauh (Faculty of Medicine, Chiang Mai University) ;
  • Nadee Chitapanarux (Faculty of Medicine, Chiang Mai University) ;
  • Pittayarat Intasuwan (Department of Anatomy, Faculty of Medicine, Chiang Mai University) ;
  • Yanumart Malatong (Department of Anatomy, Faculty of Medicine, Chiang Mai University) ;
  • Apichat Sinthubua (Department of Anatomy, Faculty of Medicine, Chiang Mai University) ;
  • Patison Palee (College of Arts, Media and Technology, Chiang Mai University) ;
  • Sakarat Na Lampang (Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University) ;
  • Pasuk Mahakkanukrauh (Department of Anatomy, Faculty of Medicine, Chiang Mai University)
  • 투고 : 2022.10.13
  • 심사 : 2022.12.05
  • 발행 : 2023.03.31
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초록

Age at death estimation has always been a crucial yet challenging part of identification process in forensic field. The use of human skeletons have long been explored using the principle of macro and micro-architecture change in correlation with increasing age. The clavicle is recommended as the best candidate for accurate age estimation because of its accessibility, time to maturation and minimal effect from weight. Our study applies pre-trained convolutional neural network in order to achieve the most accurate and cost effective age estimation model using clavicular bone. The total of 988 clavicles of Thai population with known age and sex were radiographed using Kodak 9000 Extra-oral Imaging System. The radiographs then went through preprocessing protocol which include region of interest selection and quality assessment. Additional samples were generated using generative adversarial network. The total clavicular images used in this study were 3,999 which were then separated into training and test set, and the test set were subsequently categorized into 7 age groups. GoogLeNet was modified at two layers and fine tuned the parameters. The highest validation accuracy was 89.02% but the test set achieved only 30% accuracy. Our results show that the use of medial clavicular radiographs has a potential in the field of age at death estimation, thus, further study is recommended.

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과제정보

This work was supported by the Faculty of Medicine, Chiang Mai University, grant no. 069-2565 for research funding. The authors are also gratefully thankful for the support from the Excellence Center in Osteology Research and Training Center (ORTC) with partial support from Chiang Mai University.

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