基于对3维圆束计算断层图像的视觉体积分析来诊断病变的Sivan分类系统
1Department of Oral Medicine and Radiology, Teerthanker Mahaveer Dental College and Research Centre, Moradabad, Uttar Pradesh, India. sivansathishmfds@yahoo.co.in.
Scientific reports
|December 31, 2024
概括
新的Sivan分类系统通过使用圆束计算机断层扫描 (CBCT) 的体积分析增强了病变的诊断. 该系统表现出卓越的interrater和intrarater可靠性,提高了诊断准确性和一致性.
科学领域:
- 口腔和面放射学 口腔和面放射学
- 牙科成像分析 牙科成像分析
- 诊断分类系统 诊断分类系统
背景情况:
- 准确诊断下病变对于有效的治疗计划至关重要.
- 诊断下病变的传统方法可能缺乏一致性和准确性.
- 对圆束计算机断层扫描 (CBCT) 图像的体积分析为改善诊断能力提供了潜在的机会.
研究的目的:
- 引入和验证用于诊断病变的Sivan分类系统.
- 用CBCT体积分析评估Sivan分类的可靠性和一致性.
- 将Sivan分类的诊断性能与传统方法进行比较.
主要方法:
- 开发了Sivan分类系统,将病变分类为低体积度,高体积度和正常体积度组.
- 通过使用Sivan分类,对10名评级者 (5名普通牙医,5名口腔放射科医生) 的CBCT图像进行评估.
- 通过重复评估和kappa统计,评估interrater和intrarater的可靠性.
主要成果:
- 总体而言,评价者之间的一致性很好 (kappa = 0.82).
- 在超体积 (kappa = 0.84) 和正常体积 (kappa = 0.82) 病变中观察到最高的一致性.
- 对于低体积度病变 (kappa = 0.77),发现了实质性的一致性,具有强大的内部可靠性 (kappa = 0.790.89).
结论:
- 西凡分类系统为诊断下病变提供了一个强大而可靠的框架.
- 用Sivan分类对CBCT图像进行体积测量分析可以提高诊断的准确性和一致性.
- 这种新的系统在评估病变方面显示出超越传统诊断方法的潜力.
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