通过比较德米尔吉安的方法和巴西东南部青年机器学习来估计牙科年龄
Allan Abuabara1, Thais Vilalba Paniagua Machado do Nascimento2, Kaliane Rodrigues da Cruz2
1Universidade da Região de Joinville (University of the Joinville Region- Univille), Rua Paulo Malschitzki, no 10 Campus Universitário - Distrito Industrial, Joinville, SC, 89219-710, Brazil. allan.abuabara@gmail.com.
Forensic science, medicine, and pathology
|July 11, 2025
概括
结合了德米尔吉安.
科学领域:
- 法医人类学 法医人类学
- 儿科牙科 儿科牙科
- 医疗保健中的人工智能
背景情况:
- 牙发育阶段对于儿童和青少年的年龄估计至关重要.
- 德米尔吉安的方法是评估牙成熟度的传统方法.
- 传统的牙年龄估计方法的精度存在局限性.
研究的目的:
- 评估将Demirjian的方法与机器学习 (ML) 结合起来,以更准确地估计牙年龄.
- 用牙发育来评估各种ML算法的性能,以预测使用牙发育的时间年龄.
- 为了比较基于ML的年龄估计准确度与传统的德米尔吉安方法.
主要方法:
- 对来自巴西儿童和青少年的610张数字全景放射图进行了回顾性分析.
- 应用德米尔吉安的方法来分类永久下牙的发育阶段.
- 使用交叉验证对八个ML模型 (例如,梯度增强,随机森林) 进行培训和评估.
主要成果:
- 机器学习模型在1.5年内实现了预测错误.
- 渐变增强和随机森林模型显示出优异的性能 (MAE: 0.75,RMSE: 0.92).
- 与Demirjian的方法相比,平均绝对误差 (44.03%) 和根平均平方误差 (43.56%) 显著减少.
结论:
- 将ML与Demirjian的方法集成,显著提高了牙年龄估计的准确性.
- 人工智能驱动的牙科年龄评估为临床和法医应用提供了更高的可靠性.
- 这种综合方法为年龄确定提供了更精确,更适应的工具.
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