Convolutional neural network of age-related trends digital radiographs of medial clavicle in a Thai population: a preliminary study

• 영문명: Convolutional neural network of age-related trends digital radiographs of medial clavicle in a Thai population: a preliminary study
• 발행기관: 대한해부학회
• 저자명: Phisamon Kengkard Jirachaya Choovuthayakorn Chollada Mahakkanukrauh Nadee Chitapanarux
• 간행물 정보: 『Anatomy and Cell Biology』Vol.56(1), 86~93쪽, 전체 8쪽
• 주제분류: 의약학 > 의학일반
• 파일형태: PDF
• 발행일자: 2023.03.31

영문 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.

목차

Introduction

Materials and Methods

Results

Discussion

ORCID

Author Contributions

Conflicts of Interest

Acknowledgements

References

키워드

Clavicle Age estimation Radiography Convolutional neural network