PDF(Pubmed)
Abstract：
OBJECTIVE: Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning.
METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated.
RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18.
CONCLUSIONS: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.
METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated.
RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18.
CONCLUSIONS: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.
摘要：
目标：年龄估计在个人身份识别中起着至关重要的作用，特别是在确定青少年同意年龄时。同意年龄是指个人在法律上被认为能够为性活动提供知情同意的最低年龄。这项研究的目的是通过使用牙齿发育与机器学习相结合来确定青少年是否满足14或18岁。
方法：这项研究结合了牙科评估和机器学习技术，以预测青少年是否已达到14或18岁的同意年龄。如第三磨牙的分期等因素，第三摩尔指数，并评价第二磨牙牙周膜的可见性。
结果：性能指标的差异表明，机器学习获得的后验概率在14岁时超过93％，在18岁时略低。
结论：这项研究为青少年个人鉴定法医鉴定提供了有价值的见解，强调通过将传统方法与机器学习相结合来提高该人群年龄确定准确性的潜力。它强调了保护和尊重所有有关个人尊严的重要性。
方法：这项研究结合了牙科评估和机器学习技术，以预测青少年是否已达到14或18岁的同意年龄。如第三磨牙的分期等因素，第三摩尔指数，并评价第二磨牙牙周膜的可见性。
结果：性能指标的差异表明，机器学习获得的后验概率在14岁时超过93％，在18岁时略低。
结论：这项研究为青少年个人鉴定法医鉴定提供了有价值的见解，强调通过将传统方法与机器学习相结合来提高该人群年龄确定准确性的潜力。它强调了保护和尊重所有有关个人尊严的重要性。
