在CBCT扫描中使用2D卷积神经网络对中部部成熟的自动分类
Mahshid Nik Ravesh1, Nazila Ameli1, Manuel Lagravere Vich1
1School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
Frontiers in dental medicine
|July 11, 2025
概括
一个新的2D CNN模型从CBCT扫描中准确地分类了中侧部结成熟阶段,有助于对大横向缺陷的正牙治疗计划. 这种人工智能工具提供了一个可靠和高效的替代手动口译.
科学领域:
- ортодонтика和牙科成像 牙科成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 准确的中侧 suture (MPS) 成熟评估对于治疗大横向缺陷 (MTD) 至关重要.
- 圆束计算断层扫描 (CBCT) 提供了详细的成像,但手动MPS分类是主观的和耗时的.
- 需要自动化方法来提高MPS分期的效率和客观性.
研究的目的:
- 开发和验证一个轻量级的2D卷积神经网络 (CNN) 用于自动MPS成熟分类.
- 用轴性CBCT切片评估CNN的表现,并将其与已建立的分类标准进行比较.
- 评估人工智能模型在正治疗规划中的潜在临床实用性.
主要方法:
- 使用了111名患者的CBCT图像的回顾性数据集.
- 根据安吉利利的系统,图像被分为三类:AB,C和DE.
- 通过使用准确度,F1分数和ROC分析,训练和评估了一个9层的2D CNN.
主要成果:
- 2D CNN实现了96.49%的高测试准确率.
- 各类F1分数非常出色 (AB为0.95分,C为1.00分,DE为0.95分).
- 在ROC曲线下的面积 (AUC) 显示出强的表现,特别是在成熟阶段 (0.98对于DE).
结论:
- 开发的2D CNN对于从CBCT图像中对MPS成熟阶段进行分类是准确和强大的.
- 该模型的轻量级设计和高性能使其适合实时临床决策和资源有限的设置.
- 未来的研究将集中在体积模型上,以进一步提高MPS评估的诊断可靠性.
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