牙拥挤分类网络 (DCC-Net):可解释的深度学习系统,用于在口内照片上自动分类牙拥挤
Raokaijuan Wang1,2, Yangjie Deng3, Fangyuan Cheng3
1Department of Orthodontics, Chongqing University Three Gorges Hospital, Chongqing, China.
Korean journal of orthodontics
|January 23, 2026
概括
一个新的AI系统,牙拥挤分类网络 (DCC-Net),准确地从口腔内照片分类牙拥挤. 这种工具有助于正牙医在诊断和治疗规划中,提高了经验丰富和初级医生的准确性.
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:
- 牙拥挤对于正牙诊断和拔牙决定至关重要.
- 当前的方法往往需要复杂的空间分析.
- 需要一个自动化系统,从口内摄影中对人群进行分类.
研究的目的:
- 开发一个自动化系统,牙拥挤分类网络 (DCC-Net),用于从口腔内照片中分类牙拥挤水平.
- 与传统方法相比,评估DCC-Net的诊断准确性.
- 评估DCC-Net协助对初级牙医诊断准确性的影响.
主要方法:
- DCC-Net是由细分,提取和分类模块开发的.
- 训练和测试使用了2584张口内照片的多中心数据集.
- 经验丰富的正牙医使用口腔内扫描数据确定了基本真相.
主要成果:
- DCC-Net实现了高分类准确性,对于部 (0.7232) 和下部 (0.7352) 实现了高分类准确性.
- 热图显示DCC-Net能够识别牙弧形和缺陷区域.
- 在DCC-Net的帮助下,初级牙科专家的诊断准确度提高了9.18% (大) 和12.75% (下).
结论:
- DCC-Net从口腔内照片提供精确的牙拥挤分类.
- 该系统提供了快速预测,可用于指导拔牙决策.
- DCC-Net 作为未经验的正牙医的参考,增强了医生与患者之间的沟通.
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