基于深度学习的检测,在大前的根数
Ecem Azgari1, Cem Azgari2, Hesna Sazak Öveçoğlu3
1Department of Endodontics, Institute of Health Sciences, Marmara University, İstanbul, Turkey.
International endodontic journal
|January 6, 2026
概括
深度学习模型从全景放射图中准确地预测了前根数. 一个整体模型证明了最可靠的性能,为临床决策提供了潜在的支持工具.
科学领域:
- 牙科 牙科是指牙科的专业.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 确定大前的根数对于内牙诊断和治疗规划至关重要.
- 全景放射是常见的成像方式,但根数检测可能具有挑战性.
- 深度学习为放射图像的自动化分析提供了潜力.
研究的目的:
- 评估深度学习模型在使用全景射线图检测大前的根数方面的有效性.
- 为了比较不同卷积神经网络 (CNN) 架构的性能.
主要方法:
- 一项回顾性研究使用了来自350名患者的925个前,CBCT扫描作为参考标准.
- 三个CNN模型 (AlexNet,DenseNet-121,EfficientNet-B0) 在预处理的全景图像上使用转移学习进行训练.
- 用数据增强,五倍交叉验证和独立的外部验证集来评估模型性能和通用性.
主要成果:
- 集体深度学习模型在交叉验证中获得了最高的准确性 (0.90) 和AUC (0.94).
- 在外部验证中,整体模型也表现最好 (准确率为0.87),表现优于专家临床医生 (准确率为0.82).
- 整体模型显示变化性降低和信心区间缩小,表明性能强.
结论:
- 深度学习模型,特别是整体方法,在从全景放射图中识别上前根数方面显示出可靠的性能.
- 这些人工智能系统有望作为辅助工具,帮助临床医生做出决策.
- 进一步的研究可以探索将其整合到日常牙科实践中.
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