基于CBT的抽取插座愈合评估和分层诊断决策的分级多标签分类模型,以协助植入物治疗计划
Qing Li1, Ruitong Han2, Jieying Huang3
1The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, Guangdong, China.
Journal of dentistry
|August 23, 2025
概括
这项研究介绍了HierTransFuse-Net,这是一种AI模型,用于评估CBCT扫描的牙拔愈合. 它准确地分类愈合阶段, 帮助个性化牙科植入物治疗计划.
科学领域:
- 口腔和面放射学
- 在牙科领域的人工智能
- 医学成像分析
背景情况:
- 评估口愈合对于牙科植入物规划至关重要.
- 目前的圆束计算机断层扫描 (CBCT) 方法难以区分软组织和织造骨,并且缺乏标准化的分类.
- 这限制了牙科植入物的准确治疗计划.
研究的目的:
- 开发一个基于CBCT的分层多标签分类模型.
- 提高提取后治愈评估的准确性和标准化.
- 支持个性化的牙科植入物治疗计划.
主要方法:
- 建立了一个新的分类系统,分为两级:一级 (生理与病理愈合) 和二级 (5个亚型).
- 使用HierTransFuse-Net架构,集成ResNet50和2D变压器,用于层次的多标签分类.
- 用随机森林算法进行分层诊断的原则用于治疗规划.
主要成果:
- HierTransFuse- Net的精度为0.9705,mF1得分为0.9253,其性能非常出色.
- 与临床医生相比,该模型显示出更高的诊断可靠性 (κω = 0. 9234).
- 随机森林模型预测了临床途径,准确度为81.48%和mF1得分为82.55%.
结论:
- HierTransFuse-Net有效地从CBCT图像中区分提取插座愈合状态和子类型.
- 阶层分类系统模仿临床推理,以准确地分类疾病.
- 随机森林算法可以预测临床途径, 支持个性化治疗决策.
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