使用各种机器学习技术对侧向脑图进行椎脊椎成熟度评估:一个系统的文献综述
Shailendra Singh Rana1, Bhola Nath2, Prabhat Kumar Chaudhari3
1Department of Dentistry, All India Institute of Medical Sciences, Bhatinda, Punjab, India.
Journal of oral biology and craniofacial research
|September 4, 2023
概括
机器学习模型在椎脊椎成熟评估 (CVMA) 中表现有希望,达到60-90%的准确性. 需要进一步的研究来完善这些模型,以便在dentofacial骨科中广泛的临床使用.
科学领域:
- 牙面部整形外科 牙面部整形外科
- 放射学 放射学是一门学科.
- 机器学习 机器学习
背景情况:
- 评估骨成熟度对于在dentofacial骨科治疗的最佳治疗时间至关重要.
- 椎成长评估 (CVMA) 提供了减少辐射的替代手腕X射线扫描.
- 最近的进展探索CVMA的机器学习 (ML),需要临床验证.
研究的目的:
- 系统地审查机器学习技术在使用横向头脑图的椎脊椎成熟度评估中的应用.
主要方法:
- 在主要数据库 (Ovid Medline,Embase,PubMed,CENTRAL,CDSR) 中使用PICO框架进行了系统的文献搜索.
- 还进行了灰色文献和关键牙科期刊的手动搜索.
- 包括的研究分析了ML技术,样本数据,年龄范围以及与传统CVMA方法的比较.
主要成果:
- 在被评估的25篇论文中,有13篇论文被纳入系统审查.
- 机器学习模型的准确度在60%至90%以上.
- 机器学习模型的性能显示出与人类专家评估的满意一致性和相关性.
结论:
- 机器学习模型对于检测和分类椎脊椎成熟是有效的.
- 需要对均分布的,性别平衡的样本进行进一步的研究,以提高模型的通用性.
- 经过验证的ML模型可以显著有利于牙面部整形外科的临床实践.
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