人工智能驱动的精度在牙放射分析中使用定制的CNN来进行牙编号和腔腔检测
Breno Guerra Zancan1, José Andery Carneiro1, Caio Uehara Martins1
1Department of Computing and Mathematics, FFCLRP, University of São Paulo (USP), Ribeirão-Preto, São Paulo, Brazil.
PLOS digital health
|November 20, 2025
概括
这项研究开发了一种使用卷积神经网络 (CNN) 的AI系统,用于牙科放射,准确编号牙和检测腔. 这种先进的系统提高了牙科诊断的准确性.
科学领域:
- 医疗保健中的人工智能
- 医学图像分析 医学图像分析
- 牙科诊断 牙科诊断 牙科诊断
背景情况:
- 牙科图像解释需要专业知识,面临误诊和主观分析等挑战.
- 数字牙科放射学对于诊断,治疗计划和外科手术至关重要.
- 卷积神经网络 (CNN) 显示出提高医学成像诊断准确性的潜力.
研究的目的:
- 开发一个专门的AI分析仪用于数字牙科放射学.
- 为了准确地计数牙并使用CNN检测牙腔.
- 提高牙科诊断可靠性和临床决策.
主要方法:
- 使用Inception-v3和InceptionResNet-v2模型进行图像分析.
- 在935张牙科全景射线图 (PAN) 上训练并评估了该系统.
- 数据是由巴西里贝拉诺普雷托牙科学院的牙医进行注释的.
主要成果:
- 在牙编号方面实现了高性能:0.98精度,0.98回忆,0.998准确度,0.999特异性和0.98F1得分.
- 在识别腔牙方面表现出强有力的结果:0.96精度,0.91回忆,0.94准确度,0.96特异性和0.94F1得分.
- 开发的系统显示了对现实世界牙科诊断应用的巨大潜力.
结论:
- 人工智能系统有效地解决了牙图像分析方面的挑战.
- 高性能指标表明可靠和准确的牙检测和腔位识别.
- 这项研究为牙科诊断中的AI提供了一个基准,指导未来的研究和应用.
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