一种YOLO-V5方法用于评估正常填充和悬浮填充:人工智能研究
Nilgün Akgül1, Cemile Yilmaz2, Elif Bilgir3
1Pamukkale University, Faculty of Dentistry, 1Department of Restorative Dentistry, Denizli, Türkiye.
Brazilian oral research
|October 2, 2024
概括
这项研究引入了一种AI模型,用于在全景X射线上检测有问题的牙填充物. 深度学习模型准确地识别正常和悬挂的修复,改善牙科诊断.
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:
- 牙填充对于恢复牙至关重要,但如果不适当放置,可能会导致问题.
- 悬浮的填充物可能会对牙周健康和整体牙健康产生负面影响.
研究的目的:
- 开发和评估一个深层卷积神经网络 (CNN) 来检测正常和悬挂的牙填充修复.
- 通过全景放射学来评估这些修复的患病率和分布.
主要方法:
- 一个数据集由10,480个填充和2,491个悬浮填充,从2,473个和1,850个全景放射图像中标记出来.
- 使用YOLOv5x架构进行监督学习,以训练AI模型.
- 用灵敏度,精度和F1分数来评估模型性能.
主要成果:
- 人工智能模型实现了高性能指标:正常填充剂的灵敏度为0.95,精度为0.97,F1得分为0.96.
- 对于悬浮的填充物,该模型的灵敏度为0.86,精度为0.89,F1得分为0.87.
- YOLOv5算法在细分牙科放射图和区分填充类型方面被证明是高效和准确的.
结论:
- 开发的AI模型在识别和区分正常和悬挂的牙填充物方面表现出显著的熟练程度.
- 这项技术为提高牙科放射分析的准确性和效率提供了有价值的工具.
- 准确检测填充恢复可以有助于改善患者的口腔健康结果.
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