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

基本信息

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
项目类别：
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

项目摘要

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。抽象的牙周炎是成人中最常见的非传染性疾病 (NCD) 之一，影响着 6,470 万人美国人根据 2009-2012 年的估计，目前的牙周炎评估检查方案是：对于人群水平的研究来说，全口检查（FME）效率低下或不准确，被认为是黄金。估计真实牙周炎患病率的标准，但它是资源和时间最密集的标准之一尽管口腔健康研究人员经过数十年的努力，但仍未能实现健康研究中的评估方法。使准确的部分口腔检查 (PME) 方案的开发更加实用。可实施的 PME 是以下方面的主要障碍：1) 确定全球社区健康需求，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；重要的是，新出现的证据表明使用 FME 不切实际或效率低下。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模型的支持，尽管如此，人类相关研究仍存在关于糖尿病发病风险是否增加的明确数据。牙周病患者缺乏，因为目前全口牙周炎需要资源和时间因此，尽管如此，检查仍阻碍了充分有力的前瞻性研究中牙周炎的评估。由于牙周炎-糖尿病关联的重要性，牙周措施常常被排除在大型研究之外。由于资金和后勤限制，医疗队列的目标是实现这一目标。通过制定和实施牙周炎评估，将牙周炎评估纳入社区和人口层面的监测验证计算增强的 PME 方法用于牙周炎评估的有效性。由一支强大的跨学科团队组成，他们在流行病学、全球健康、生物统计学等领域具有互补的专业知识和机器学习，并得到超过 25,000 名连续参与者的广泛 FME 数据集的支持 NHANES、西班牙社区健康研究 (HCHS) 以及口腔感染、葡萄糖不耐受和胰岛素阻力研究（ORIGINS），该提案将追求两个具体目标：1）在计算上增强利用机器学习在牙周炎分类中的新颖实现来预测 PME， 2）根据现有 PME 和“黄金分类器”评估增强型 PME 分类器的性能标准”FME 研究牙周炎与血糖状态之间的关联。所提出的方法得到了强有力的初步数据的支持，表明支持向量机（SVM）分类器将牙周炎预测的灵敏度从 54% 提高了（从牙周病灶中“简单”计算患病部位）目前使用将定义 PME 减半）至 90%（SVM 增强疾病分类），同时保持可接受的误报率为 3%，最终，这种增强型 PME 将用于组装大型产品。以具有时间成本效益的方式治疗牙周炎人群，从而改变非传染性疾病领域流行病学和全球健康监测。