从诊所到社区:一种可解释的人工智能框架,用于检测乳虫,以支持公共卫生牙科
1Department of Dental Public Health, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
European journal of dentistry
|February 20, 2026
概括
这项研究引入了一个人工智能框架,用于准确,可解释的牙牙下的检测. 深度学习模型有助于早期诊断和非侵入性治疗,改善临床结果.
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:
- 牙质皮是一种广泛的口腔疾病,需要早期检测才能有效地进行非侵入性治疗.
- 传统的诊断方法,如视觉检查和放射学,对初始病变的敏感性和检查员的可变性有局限性.
研究的目的:
- 开发一个高效的,可解释的深度学习框架,用于在多个严重程度级别下自动分类牙损.
- 确保自动化虫检测的临床适用性和透明度.
主要方法:
- 使用12,000张牙部图像的数据集,训练了修改后的EfficientNetB0和修改后的MobileNetV2模型.
- 一个以注意力为导向的融合机制将模型与梯度加权类激活映射 (Grad-CAM) 结合起来,以实现可解释性.
主要成果:
- 合并模型实现了96.92%的准确性,F1得分为96.92,ROC AUC为99.34%.
- 个别模型 (修改后的EfficientNetB0和MobileNetV2) 的高准确率分别为96.33%和96.25%.
- 该框架展示了低推断时间,适合实时临床应用,相邻阶段之间的错误分类最小.
结论:
- 拟议的深度学习框架提供了准确,可解释和高效的牙质的检测.
- 该系统的速度和清晰度支持临床决策,促进早期干预和最少侵入性治疗.
- 建议通过多中心和多模式数据进行进一步验证,以提高概括性.
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