口腔微生物社区和金字塔场景解析基于网络的牙周炎风险预测
Zhuo Zhao1, Xiaoxu Liu2, Mengting Li3
1Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China; Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases, College of Stomatology, Xi'an Jiaotong University, Xi'an, China; State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
International dental journal
|November 29, 2024
概括
这项研究引入了一种新的深度学习方法,使用牙斑微生物准确预测牙周炎风险,使早期干预和预防不可逆转损伤成为可能.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 牙周炎 (PD) 是一种常见的慢性炎症性牙疾病,通常是晚诊断的,导致严重的组织损伤和全身问题.
- 早期发现PD风险对于预防不可逆转的牙损失和相关的健康并发症至关重要.
研究的目的:
- 开发一种可靠和有效的方法来早期识别牙周炎风险.
- 将深度学习与微生物分析结合起来,以创建一种新的牙周炎风险评分 (PRS).
主要方法:
- 利用了来自90名健康对照组和514名PD患者的牙斑微生物分析数据.
- 采用了在预处理数据上训练的金字塔场景解析网络 (PSPNet) 模型来生成PRS.
- 通过比较PSPNet衍生的PRS (PRSPSPnet) 与实际计算的PRS (PRSActual) 来评估模型准确性.
主要成果:
- 确定了27个PD风险预测的关键指标.
- 该模型在预测和实际PRS之间实现了0.027的低平均绝对误差.
- 证明了高效率,平均计算时间为每样10-5秒.
结论:
- 微生物分析和PSPNet的整合为早期PD风险查提供了一个非侵入性,准确和高效的工具.
- 这种方法可以帮助预防牙周损伤,改善口腔健康结果,减少医疗负担.
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