Anat Cell Biol.  2023 Mar;56(1):86-93. 10.5115/acb.22.205.

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

• Kengkard P
• Choovuthayakorn J
• Mahakkanukrauh C
• Chitapanarux N
• Intasuwan P
• Malatong Y
• Sinthubua A
• Palee P
• Lampang SN
• Mahakkanukrauh P

• KMID: 2540988
• DOI: http://doi.org/10.5115/acb.22.205

Abstract

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.

Keyword

Clavicle; Age estimation; Radiography; Convolutional neural network

Figure

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  Fig. 1 The radiographs of 8 pairs clavicular samples, red box=3×3 cm2 region of interest.

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  Fig. 2 Confusion matrix of the predicted age range.

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  Fig. 3 Graph of training accuracy and loss for the final CNN model. CNN, convolutional neural network.

Reference

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