在CBCT衍生的伪泛相图像上使用深度学习来评估永久性第一牙中的上性:一项回顾性研究
Suna Deniz Bostanci1, Zeliha Hatipoğlu Palaz2, Kevser Özdem Karaca3
1Private Practice, 59860 Tekirdağ, Turkey.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
深度学习卷积神经网络 (CNN) 通过使用牙科放射图准确地评估牙的尖端通透性. 这种人工智能 (AI) 应用程序有助于内牙科和年龄估计.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 根部发育和尾关闭在内牙科,牙创伤和年龄估计中至关重要.
- 数字健康和人工智能为牙科诊断提供了新的工具.
研究的目的:
- 评估深度学习的有效性 卷积神经网络 (CNN) 用于自动评估永久第一牙中的顶峰区域状态.
- 突出AI在牙科中的数字健康应用.
主要方法:
- 对262个圆束计算机断层扫描 (CBCT) 扫描的回顾性分析.
- 147个匿名的牙图像从伪全景放射图片中剪切.
- 评估CNN模型的性能使用准确度,精度,回忆,F1得分和ROC曲线与AUC.
主要成果:
- CNNs获得了准确度,回忆和F1得分,开放根的0.79和封闭根的0.81.
- 所有指标的宏观平均值为0.80.
- 整体精度和曲线下的面积 (AUC) 也为0.80.
结论:
- 在有效评估顶端通透性方面,CNN显示出显著的潜力.
- 基于人工智能的伪全景放射图像图像分析是牙科中一个可行的工具.
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