使用基于多个生物指标的机器学习来预测骨成熟度
Man Guo1, Baraa Daraqel2, Dongqing Ai1
1Chongqing Key Laboratory of Oral Diseases, Chongqing Municipal Health Commission Key Laboratory of Oral Biomedical Engineering, Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, The Affiliated Stomatological Hospital of Chongqing Medical University, Chongqing, China.
概括
这项研究开发了一种机器学习模型,使用牙成熟阶段 (DMS),宫形态,年龄和性别来预测骨成熟度. 该模型准确预测成熟阶段,为传统方法提供了切实可行的替代方案.
科学领域:
- 矯正牙科 矯正牙科是一種矯正牙科.
- 放射学 放射学是一门学科.
- 机器学习 机器学习
背景情况:
- 准确的骨成熟度评估对于正牙治疗规划和生长调节至关重要.
- 传统的方法,如手腕X射线,可能是耗时的,可能并不总是切实可行的.
- 开发高效准确的预测模型对于优化患者护理至关重要.
研究的目的:
- 开发一个全面的机器学习 (ML) 模型来预测骨成熟阶段.
- 评估ML模型的预测性能,包括椎脊椎形态,牙成熟阶段 (DMS),性别和年龄.
- 为了比较不同的ML算法对骨成熟度预测的有效性.
主要方法:
- 利用了860名患者的数据集,进行横向脑电图,全景和手腕X射线图.
- 将基线模型与包含DMS,性别和年龄的增强模型进行比较.
- 评估了六个ML算法,CatBoost表现出卓越的性能,并分析了特征的重要性.
主要成果:
- 与单独的宫形态学相比,将DMS,性别和年龄纳入预测准确度显著提高.
- CatBoost模型实现了高性能指标,包括0.924的AUC和0.752.75的F1得分.
- 牙成熟阶段,特别是下第二,被确定为骨成熟的强有力的预测因素.
结论:
- 一个新的和临床上实用的ML模型用于骨成熟度的预测是使用常规获得的正统牙科记录开发的.
- 将牙成熟阶段与宫形态,年龄和性别相结合,可以提高预测的准确性.
- 这种ML模型为确定骨成熟度提供了一种可靠和有效的替代传统手腕评估方法.
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