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.
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。