Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population

Weijie Shan · Yunshu Sun · Leyan Hu · Jie Qiu · Miao Huo · Zikang Zhang +4 more

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Forensic Anthropology and Bioarchaeology Studies Dental Radiography and Imaging Face recognition and analysis

Abstract

Age estimation based on the mineralized morphology of teeth is one of the important elements of forensic anthropology. To explore the most suitable age estimation protocol for adolescents in the South China population, 1477 panoramic radiograph images of people aged 2-18 years in the South were collected and staged by the Demirjian mineralization staging method. The dental ages were estimated using the parameters of the Demirjian and Willems. Mathematical optimization and machine learning optimization were also performed in the data processing process in an attempt to obtain a more accurate model. The results show that the Willems method was more accurate in the dental age estimation of the southern China population and the model can be further optimized by reassigning the model through a nonintercept regression method. The machine learning model presented excellent results in terms of the efficacy comparison results with the traditional mathematical model, and the machine learning model under the boosting framework, such as gradient boosting decision tree (GBDT), significantly reduced the error in dental age estimation compared to the traditional mathematical method. This machine learning processing method based on traditional estimation data can effectively reduce the error of dental age estimation while saving arithmetic power. This study demonstrates the effectiveness of the GBDT algorithm in optimizing forensic age estimation models and provides a reference for other regions to use this parameter for age estimation model establishment, and the lightweight nature of machine learning offers the possibility of widespread forensic anthropological age estimation.

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References

1973A new system of dental age assessment.2001Dental Age Estimation in Belgian Children: Demirjian's Technique Revisited 1998Dental age assessment: The applicability of Demirjian's method in South Indian children 2010Assessment of dental maturity of western Chinese children using Demirjian's method 2014Dental age assessment in 7–14-year-old Chinese children: Comparison of Demirjian and Willems methods 2021Machine learning assisted Cameriere method for dental age estimation 2019Sex estimation: a comparison of techniques based on binary logistic, probit and cumulative probit regression, linear and quadratic discriminant analysis, neural networks, and naïve Bayes classification using ordinal variables 2022With or without human interference for precise age estimation based on machine learning?

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2024Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs — a systematic review 2024Do machine learning methods solve the main pitfall of linear regression in dental age estimation?2025Development and evaluation of a deep learning-based system for dental age estimation using the demirjian method on panoramic radiographs 2025A review of the literature on the applications of machine learning in forensic anthropology

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