开发使用图像识别与深度学习的舌头涂层状态评估
Jumpei Okawa1, Kazuhiro Hori1, Hiromi Izuno2
1Division of Comprehensive Prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan.
Journal of prosthodontic research
|September 28, 2023
概括
这项研究开发了一种人工智能支持的图像识别网络,用于评估舌头涂层状态. 该网络准确地检测舌头,并对涂层进行分类,从而实现简单,详细的评估.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 评估舌头涂层对于诊断各种健康状况至关重要.
- 需要客观和详细的舌膜涂层评估方法.
- 当前的方法可能是主观的,耗时的.
研究的目的:
- 开发一个图像识别网络,用于自动化舌头涂层状态评估.
- 为舌头检测和舌头涂层分类创建单独的网络.
- 验证开发网络的准确性和一致性.
主要方法:
- 实施了两个卷积神经网络 (CNN):You-Only-Look-Once (YOLO) v2与ResNet-50用于舌头检测,以及ResNet-18用于舌头涂层分类.
- 研究人员使用了251名成人和144名老年人的数字舌头图像.
- 舌头涂层由小组成员在7x7网格上进行分级,并计算舌头涂层指数 (TCI) 以与网络输出进行比较.
主要成果:
- 舌头检测网络实现了0.885±0.081.01的高交叉点在联合 (IoU) 上.
- 舌头涂层分类网络与专家小组分类 (kappa系数=0.826) 和TCI (类内相关系数=0.807) 强烈一致.
结论:
- 人工智能驱动的图像识别提供了一种简单而详细的方法来评估舌头涂层.
- 开发的网络显示了临床应用的高精度和可靠性.
- 这项技术有可能提高与舌头涂层异常相关的诊断能力.
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