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Machine Learning Supported the Modified Gustafson’s Criteria for Dental Age Estimation in Southwest China

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Abstract

Adult age estimation is one of the most challenging problems in forensic science and physical anthropology. In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson’s criteria for dental age estimation. In this retrospective study, a total of 851 orthopantomograms were collected from patients aged 15 to 40 years old. The secondary dentin formation (SE), periodontal recession (PE), and attrition (AT) of four mandibular premolars were analyzed according to the modified Gustafson’s criteria. Ten ML models were generated and compared for age estimation. The partial least squares regressor outperformed other models in males with a mean absolute error (MAE) of 4.151 years. The support vector regressor (MAE = 3.806 years) showed good performance in females. The accuracy of ML models is better than the single-tooth model provided in the previous studies (MAE = 4.747 years in males and MAE = 4.957 years in females). The Shapley additive explanations method was used to reveal the importance of the 12 features in ML models and found that AT and PE are the most influential in age estimation. The findings suggest that the modified Gustafson method can be effectively employed for adult age estimation in the southwest Chinese population. Furthermore, this study highlights the potential of machine learning models to assist experts in achieving accurate and interpretable age estimation.

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Availability of Data and Material

The data and material that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (grant number 81971801), the Key Research and Development Program of Sichuan Province of China (grant number 2022YFS0530), and the Fundamental Research Funds for the Central Universities (grant numbers 2021SCU12079, 2023SCU12037).

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Author notes

  1. Xinhua Dai and Anjie Liu have contributed equally to this study.

Authors and Affiliations

  1. West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People’s Republic of China

    Xinhua Dai, Junhong Liu, Mengjun Zhan, Lei Shi, Xinyu Huang, Zhenhua Deng & Fei Fan

  2. Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China

    Xinhua Dai

  3. Department of Oral Radiology, College of Stomatology, Sichuan University, West China, Chengdu, 610041, People’s Republic of China

    Yuanyuan Liu

  4. College of Computer Science, Sichuan University, Chengdu, 610064, People’s Republic of China

    Wenchi Ke & Hu Chen

  5. University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Anjie Liu

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  1. Xinhua Dai
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Contributions

Xinhua Dai: conceptualization, methodology, writing — original draft preparation, data curation, writing — review and editing. Anjie Liu: conceptualization, methodology, formal analysis and investigation, writing — review and editing. Junhong Liu: methodology, formal analysis and investigation, writing — review and editing. Mengjun Zhan: formal analysis and investigation, writing — review and editing, funding acquisition, resources, supervision. Yuanyuan Liu: formal analysis and investigation, writing — review and editing. Wenchi Ke: formal analysis and investigation, writing — review and editing. Lei Shi: formal analysis and investigation, writing — review and editing. Xinyu Huang: formal analysis and investigation, writing — review and editing. Hu Chen: methodology, formal analysis and investigation, software, validation, writing — review and editing. Zhenhua Deng: conceptualization, methodology, writing — review and editing, software, validation, funding acquisition, resources, supervision. Fei Fan: conceptualization, methodology, formal analysis and investigation, software, validation, data curation, writing — review and editing, funding acquisition, resources, supervision.

Corresponding author

Correspondence to Fei Fan.

Ethics declarations

Ethics Approval

Approval was obtained from the ethics committee of Sichuan University. And informed consent was waived because of the retrospective nature. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Research Involving Human Participants and/or Animals

Human participants.

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Informed consent was waived because of the retrospective nature.

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Informed consent was waived because of the retrospective nature.

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The authors declare no competing interests.

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Dai, X., Liu, A., Liu, J. et al. Machine Learning Supported the Modified Gustafson’s Criteria for Dental Age Estimation in Southwest China. J Digit Imaging. Inform. med. 37, 611–619 (2024). https://doi.org/10.1007/s10278-023-00956-0

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