基于AI的二进制分类的准确性,用于检测混合牙阶段的缺陷
Kengo Oka1, Saki Uemura1, Satoru Morishita1
1Advanced Oral Health Science Research Laboratories, Research and Development Headquarters, LION Corporation, Edogawa-ku, Tokyo, Japan.
PloS one
|December 9, 2025
概括
人工智能模型可以在混合牙期间检测儿童的缺陷. 牙图像是关键,口腔习惯对于某些类型也很重要,显示了早期查的希望.
科学领域:
- 牙科 牙科 牙科 牙科
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 恶性闭塞是一种常见的牙异常,需要早期检测和干预.
- 需要一个工具来帮助早期牙的评估和患者的动机.
研究的目的:
- 评估开发人工智能模型的可行性,用于在混合牙阶段检测儿童的缺陷.
主要方法:
- 一项可行性研究利用了519名小学儿童的横截面数据.
- 人工智能二进制分类模型是使用自动机器学习平台开发的,用于识别深咬,大突起和拥挤.
- 模型性能使用准确度,精度,F1得分和AUC等指标进行评估.
主要成果:
- 深咬分类模型实现了高准确度 (AUC>0.90),而大突起和拥挤模型显示中度准确度 (AUC>0.70).
- 牙图像是所有模型中最重要的因素.
- 口服习惯问卷数据对于大突起和拥挤检测很重要,显示出显著的关联.
结论:
- 基于人工智能的二元分类模型显示出潜在的有效选工具,用于检测混合牙中的缺陷.
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