Integrating periodontitis assessment in medical research using computationallyenhanced classification

使用计算增强分类将牙周炎评估纳入医学研究

• 批准号: 10901243
• 负责人: Weihua Guan
• 金额: $ 18.89万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-08 至 2024-08-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10901243
• 关键词: Address; Adult; Affect; Age; American; Biomedical Research; Biometry; Cardiovascular Diseases; Chronic Disease; Classification; Clinical Research; Clinical Trials; Cohort Studies; Communities; Community Health; Computing Methodologies; Correlative Study; Data; Data Set; Dental Research; Development; Diabetes Mellitus; Disease; Ensure; Epidemiology; Equation; Ethnic Origin; Etiology; Evaluation; Exclusion; Exploratory/Developmental Grant; Funding; Glucose Intolerance; Goals; Health; Hispanic Community Health Study; Hispanic Community Health Study/Study of Latinos; Hispanic Populations; Human; Hypertension; Inflammation; Inflammatory; Insulin Resistance; Latino Population; Light; Logistics; Machine Learning; Measures; Medical; Medical Research; Methodology; Methods; Minority Groups; Modeling; National Health and Nutrition Examination Survey; Non-Insulin-Dependent Diabetes Mellitus; Oral cavity; Oral health; Participant; Pathogenesis; Patients; Performance; Periodontic specialty; Periodontitis; Population; Population Heterogeneity; Population Study; Predictive Value; Prevalence; Productivity; Prospective Studies; Protocols documentation; Public Health; Race; Records; Research; Research Personnel; Research Project Grants; Resource Allocation; Resource-limited setting; Resources; Sampling; Scientist; Site; Specificity; Surface; Surveys; Testing; Thinking; Time; Tooth Loss; Training; Uncertainty; Validation; Work; candidate identification; clinical application; cohort; computer science; cost effective; diabetes risk; disease classification; global health; improved; innovation; large datasets; learning algorithm; machine learning method; multidimensional data; novel; oral infection; pre-clinical; public health relevance; statistics; support vector machine; virtual

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.

使用计算增强技术将牙周炎评估纳入医学研究 分类. 摘要 牙周炎是成人中最流行的非传染性疾病之一，影响6470万人 根据2009-2012年的估计。目前用于牙周炎评估的检查方案是 对于人群水平的研究来说效率低下或不准确。全口检查（FME）被认为是黄金 标准估计真正的牙周炎患病率，但它是最资源和时间密集型 健康研究中的评估方法。尽管经过几十年的努力，口腔健康研究人员仍然无法 使准确的部分口腔检查（PME）协议的制定实用化。缺乏一个 可实施的PME是一个主要障碍：1）确定全球社区卫生需求，2）确定公共卫生需求， 卫生资源分配和3）在疾病关联研究中实施牙周炎措施;设置 在利用FME不切实际或效率低的情况下。重要的是，新出现的证据表明， 炎症在2型糖尿病发病机制中的作用得到了可靠的临床前因果关系模型的支持， 人类相关研究。尽管如此，关于是否存在糖尿病发病风险增加的确切数据， 牙周病患者缺乏，因为目前的资源和时间要求全口牙周炎 检查阻碍了足够有力的前瞻性研究中的牙周炎评估。因此尽管 牙周炎的重要性，糖尿病协会，牙周措施往往被排除在大 由于资金和后勤方面的限制，医疗队的人数减少。本申请的目的是使 牙周炎评估在社区和人群水平监测中的整合， 验证计算增强的PME方法用于牙周炎评估的高有效性。进行 由一个强大的跨学科团队，在流行病学，全球卫生，生物统计学 和机器学习，并由超过25，000名连续参与者的广泛FME数据集支持。 NHANES，西班牙裔社区健康研究（HCHS）和口服感染葡萄糖不耐症和胰岛素 阻力研究（ORIGINS），这项建议将追求两个具体目标：1）计算增强 利用牙周炎分类中机器学习的新实现预测PME， 以及2）评估增强的PME分类器针对现有PME和“黄金”的性能 标准”FME在调查牙周炎和血糖状态之间的关联。的可行性 所提出的方法是支持强大的初步数据显示，支持向量机（SVM） 分类器提高了牙周炎预测的敏感性，从54%（“天真”计数患病部位，从一个 目前使用的一半减少定义PME）到90%（SVM增强疾病分类），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 以时间成本效益高的方式， 流行病学和全球卫生监测。