一个多式模式的虫查使用口腔内图像和问卷
Zhuoying Liu1, Junjie Li1, Siwei Wang1
1Department of Preventive Dentistry, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong, China.
International dental journal
|February 6, 2026
概括
一个新的深度学习模型整合了牙科图像和问卷,改善了儿童早期虫检测. 这种多式联络方法可以提高早期的回忆力,为人口层面的牙科查提供了一个有前途的工具.
科学领域:
- 人工智能在牙科中的应用
- 医学成像分析 医学成像分析
- 公共卫生 公共卫生
背景情况:
- 牙损是一种普遍的儿童疾病,需要有效的查方法.
- 目前的查通常依赖于视觉检查,这可能会错过早期迹象.
- 深度学习为自动化和准确的牙科诊断提供了潜力.
研究的目的:
- 开发和评估一个多式联网深度学习模型用于儿科牙虫查.
- 整合口内照片和问卷数据,以提高诊断性能.
- 将多式模式与仅图像的单式模式进行比较.
主要方法:
- 从儿童中收集了7671张口内图像 (闭口和光滑表面).
- 使用口腔健康问卷以及临床图像标签 (无病,早期,中度至重度).
- 训练并验证了一种多模式深度学习模型,将其与仅使用图像模型进行比较,使用准确度,精度,回忆,F1分数和ROC-AUC.
主要成果:
- 多式模式模型显示了高准确性 (例如,无虫的闭口表面的92.3%) 和ROC-AUC (例如,无虫的闭口表面的0.967).
- 对于光滑表面的性能也很强,具有高精度和ROC-AUC值.
- 整合问卷数据显著改善了早期的回忆力,而不会降低整体准确性.
结论:
- 多式联网深度学习模型在早期检测牙损伤方面表现优于仅图像模型.
- 这种智能查模型显示了在人口层面上虫监测的潜力.
- 建议进一步进行大规模的多中心验证,以确认可通用性.
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