Integrating periodontitis assessment in medical research using computationallyenhanced classification

利用计算增强技术将牙周炎评估整合到医学研究中 分类。 摘要 牙周炎是成人中最常见的非传染性疾病之一，影响6470万人 美国人基于2009-2012年的估计。目前用于牙周炎评估的检查方案是 对于人口水平的研究来说，效率低或不准确。全口检查(FME)被认为是黄金 评估真实牙周炎患病率的标准，但它是最耗费资源和时间的标准之一 卫生研究中的评估方法。尽管经过几十年的努力，口腔健康研究人员仍未能 使准确的部分口腔检查(PME)协议的开发实用化。缺乏一种 可实施的PME是以下方面的主要障碍：1)确定全球社区卫生需求；2)确定公众 卫生资源分配和3)在疾病关联研究中实施牙周炎措施；环境 使用FME不切实际或效率低下的情况。重要的是，新出现的证据表明牙周 强健的临床前病因模型支持2型糖尿病发病机制中的炎症 与人类相关的研究。尽管如此，关于糖尿病发病风险增加的明确数据是否存在于 牙周患者缺乏，因为目前的资源和时间需要全口牙周炎 在动力充足的前瞻性研究中，检查阻碍了牙周炎的评估。因此，尽管 牙周炎与糖尿病关系的重要性，牙周测量往往被排除在大范围之外 由于资金和后勤方面的限制，医疗队受到了限制。此应用程序的目标是使 牙周炎评估在社区和人口水平监测中的整合 计算增强的PME方法用于牙周炎评估具有较高的有效性。已进行 由一支强大的跨学科团队组成，在流行病学、全球卫生、生物统计学等领域具有互补的专业知识 和机器学习，并由超过25,000名连续 NHANES、西班牙裔社区健康研究(HCHS)与口腔感染、糖耐量异常和胰岛素 阻力研究(Origins)，这项提议将追求两个具体目标：1)从计算上增强 利用机器学习的新实现在牙周炎分类中预测PME， 以及2)评估增强型PME分类器相对于现有的PME和“黄金”的性能 标准“FME研究牙周炎和血糖状况之间的关系。这项计划的可行性 所提出的方法得到了强大的初步数据的支持，这些数据表明，支持向量机(SVMs) 分类器将牙周炎预测的敏感度从54%提高到54%(从 当前使用的减半清晰度PME)至90%(支持向量机增强的疾病分类)，同时保持 可接受的假阳性率为3%。最终，这种增强的PME将用于组装大型 以符合时间成本效益的方式治疗牙周炎患者，从而改变非传染性疾病领域 流行病学和全球健康监测。

Integrating periodontitis assessment in medical research using computationally enhanced classification. Abstract Periodontitis is one of the most prevalent non-communicable diseases (NCDs) in adults affecting 64.7-million Americans based on 2009-2012 estimates. Current examination protocols for periodontitis assessment are either inefficient or inaccurate for population-level studies. Full-mouth examination (FME) is considered the gold standard for estimating true periodontitis prevalence, however it is among the most resource- and time-intensive assessment methods in health-research. Despite decades of efforts, oral health researchers have not been able to pragmatize the development of an accurate partial-mouth examination (PME) protocol. Lack of an implementable PME is a major barrier for: 1) identifying community health needs globally, 2) determining public health resource allocation and 3) implementing periodontitis measures in disease association studies; settings where it is impractical or inefficient to utilize FME. Importantly, emerging evidence has implicated periodontal inflammation in the pathogenesis of type 2 diabetes supported by robust pre-clinical causation models and human correlative studies. Nonetheless, definitive data on whether an increased risk for diabetes onset exists in periodontal patients is lacking because current resource and time demanding full-mouth periodontitis examinations hinder periodontitis assessment in adequately powered prospective studies. Therefore, despite the importance of periodontitis-diabetes associations, periodontal measures are often excluded from large medical cohorts due to funding and logistics limitations. The objective in this application is to enable the integration of periodontitis assessment in community and population level surveillance by developing and validating a computationally enhanced PME method for periodontitis assessment with high validity. Conducted by a strong transdisciplinary team with complementary expertise in epidemiology, global health, biostatistics and machine learning, and supported by an extensive FME dataset of over 25,000 participants of the continuous NHANES, the Hispanic Community Health Study (HCHS) and the Oral Infections Glucose Intolerance and Insulin Resistance Study (ORIGINS), this proposal will pursue two specific aims: 1) to computationally enhance the prediction of PME utilizing the novel implementation of machine learning in periodontitis classification, and 2) to assess the performance of the enhanced PME classifier against existing PMEs and “gold standard” FME in investigating the association between periodontitis and glycemic status. The feasibility of the proposed approach is supported by strong preliminary data showing that a Support Vector Machines (SVMs) classifier enhanced the sensitivity of periodontitis prediction from 54% (“naive” counting of diseased sites from a currently used half-reduced definition PME) to 90% (SVM-enhanced disease classification) while maintaining an acceptable false positive rate of 3%. Ultimately, this enhanced PME will be utilized for assembling large populations with periodontitis in a time-cost-effective manner thereby transforming the fields of NCDs epidemiology and global health surveillance.