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.

使用计算增强的医学研究中整合牙周炎评估 分类。 抽象的 牙周炎是影响6,470万的成年人中最普遍的非通信疾病（NCD）之一 根据2009 - 2012年的估计，美国人。当前的牙周炎评估方案是 人群级别的研究效率低下或不准确。全口检查（FME）被认为是黄金 估计真实牙周炎患病率的标准，但是它是资源最多和耗时的 卫生研究中的评估方法。尽管努力了数十年，但口腔健康研究人员仍无法 用来阐述准确的部分口检查（PME）方案的开发。缺乏 可实施的PME是：1）在全球范围内确定社区健康需求，2）确定公众 健康资源分配和3）疾病协会研究中实施牙周炎措施；设置 使用FME不切实际或无效。重要的是，新兴证据已经实施了牙周 由强大的临床前病毒模型和2型糖尿病的发病机理中的炎症 人类矫正研究。但是，关于是否存在糖尿病发作风险增加的确定性数据 缺乏牙周病患者，因为当前的资源和时间要求全口牙周炎 检查阻碍了适当动力的前瞻性研究中的牙周炎评估。因此，多皮 牙周炎糖尿病关联的重要性，牙周措施通常被排除在大的 由于资金和物流限制，医疗队列。此应用程序的目的是启用 通过发展和 验证具有高有效性的牙周炎评估的计算增强PME方法。实施 由一个强大的跨学科团队，具有流行病学，全球健康，生物统计学专业知识 和机器学习，并得到了25,000多个连续参与者的广泛FME数据集的支持 NHANES，西班牙裔社区健康研究（HCHS）以及口腔感染葡萄糖不耐症和胰岛素 抵抗研究（起源），该提案将追求两个具体的目的：1）计算增强 使用新颖的机器学习在牙周炎分类中预测PME， 2）评估对现有PME和“黄金的增强PME分类器的性能 标准“ FME调查牙周炎与血糖状态之间的关联。 建议的方法得到强大的初步数据的支持，表明支持向量机（SVM） 分类器从54％提高了牙周炎预测的敏感性（从A 目前使用半降低的定义PME）至90％（SVM增强疾病分类），同时保持 可接受的误报率为3％。最终，这种增强的PME将用于组装大型 患有牙周炎的人群以时间成本有效的方式转化了NCD的田地 流行病学和全球健康监测。