使用回归模型预测多余的水泥残留物,以避免植入周围疾病:一种体外研究
Febina Josephraj1, Vidyashree Nandini Venugopal2, Varshini Karthik1
1Department of Biomedical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India.
概括
预测牙植入程序后的余水泥残留物 (ECR) 对于预防并发症至关重要. 这项研究开发了一种成像模型,以准确检测和量化ECR,改善患者的治疗结果.
科学领域:
- 牙科植入物学 牙科植入物学
- 生物材料科学 生物材料科学
- 医疗成像医学成像
背景情况:
- 牙植入物修复过程中的多余水泥会导致牙周植入物粘膜炎和牙周植入物炎.
- 准确预测和去除多余的水泥残留物 (ECR) 对于预防植入物并发症至关重要.
研究的目的:
- 开发一个回归模型,利用来自八边形表面成像方法的像素值来预测ECR.
- 评估ImageJ软件在牙科植入物修复中检测ECR的有效性.
主要方法:
- 在实验室中使用了一种具有牙模仿,支柱和冠状的模型进行凝固.
- 使用计算机化平面测量方法 (CPM) 和称重方法量化过量的水泥残留物 (ECR).
- 摄影评估和回归分析在十个水泥植入物上进行.
主要成果:
- ECR量从0.3到21毫克不等,平均为5.69毫克.
- 在远端, mesiobuccal 和 mesial 表面上观察到更高的 ECR 度.
- 在CPM和权衡方法之间发现了强烈的相关性 (r=0.786),验证了回归模型的预测.
结论:
- 八角形表面成像方法有效地捕捉了植入物凝固区域的ECR信息.
- ImageJ是检测ECR的可行工具,开发的回归模型可以准确预测ECR.
- CPM和称重方法之间的一致性支持回归模型用于管理ECR的临床适用性.
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