Automatic identification of early bone loss patterns from radiographs invisible to human eyes for early periodontal disease diagnosis and prevention

从人眼看不见的射线照片中自动识别早期骨质流失模式，用于早期牙周病的诊断和预防

• 批准号: 10723693
• 负责人: Jay Patel
• 金额: $ 16.09万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723693
• 关键词: Adult; Affect; Algorithms; Alveolar Bone Loss; American; Area Under Curve; Artificial Intelligence; Assessment tool; Biological; Bone Density; Censuses; Clinical; Clinical Decision Support Systems; Computer Vision Systems; Computer software; Data; Data Set; Databases; Decision Making; Dental; Dental Clinics; Dental Informatics; Dental Records; Dental Schools; Dentistry; Dentists; Detection; Deterioration; Development Plans; Diagnosis; Diagnostic; Disease; Documentation; Early Diagnosis; Early identification; Electronics; Ensure; Evidence based practice; Exposure to; Eye; Frequencies; Funding; Goals; Habits; Health; Health Care Costs; Health Status; Human; Image; Image Enhancement; Lesion; Literature; Manuals; Measurement; Measures; Medical Records; Medicine; Mentors; Methods; Minerals; Modeling; National Institute of Dental and Craniofacial Research; Natural Language Processing; Noise; Oral; Oral health; Pain; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Performance; Periodontal Diseases; Periodontitis; Prevalence; Prevention; Prevention strategy; Preventive; Preventive care; Privatization; Process; Prognosis; Psyche structure; Quality of life; Radiology Specialty; Reporting; Research; Research Personnel; Risk; Risk Assessment; Scientist; Systemic disease; Technology; Testing; Therapeutic; Time; Tooth Diseases; Tooth Loss; Training; Translating; Translations; Visit; accurate diagnosis; alveolar bone; bone; bone loss; career development; clinical practice; community based participatory research; data quality; deep learning algorithm; deep learning model; demographics; design; diagnostic accuracy; disability; disease diagnosis; disorder prevention; empowerment; high risk; image processing; imaging informatics; improved; lectures; novel; novel strategies; periapical; predictive modeling; prevent; programs; protective factors; prototype; psychologic; radiological imaging; skills; social; social health determinants; tool; treatment planning

Abstract: Periodontitis is the second most prevalent but preventable dental disease affecting over 64 million Americans and responsible for tooth loss, functionality limitations, pain, and poor quality of life. Thus, early diagnosis and preventive therapeutics are imperative in clinical practice to prevent disease initiation and progression. However, by the time dentists can observe the first bone loss patterns in radiographs to diagnose periodontitis, 30-50% deterioration (periodontal bone damage) has already occurred, which is not visible to human eyes. Clinical decision support systems are designed to identify high-risk periodontitis patients for prevention; however, they are not widely used in clinical practice because of the suboptimal prediction performance and lack of diverse predictive features (early bone loss lesions) for prediction. Therefore, there is an unmet need for a tool that can detect early bone loss patterns invisible to human eyes to alert dentists for early diagnosis and preventive care. Dr. Patel has developed an artificial intelligence (AI) empowered prediction model for periodontitis that utilizes more than 150 distinct variables (e.g., social determinants of health, medical records, lab reports, CDC census data, financial data, etc.) for prediction, which aren't well understood in the existing literature. However, this model lacks dental imaging data such as bone pattern, bone density, pixel intensity, and other imaging predictive features, which have a high potential to improve prediction accuracy. The early bone mineral changes in alveolar bone for early diagnosis have been studied in biological studies; however, the transition of these findings at the chairside is limited. AI and computer vision can bridge this gap and help identify early bone loss patterns from radiographs invisible to human eyes. Therefore, the objective of this project is to develop three automated computer vision algorithms: 1) to improve the extraction of diagnostically meaningful information from periapical radiographs, 2) to determine the extent of bone loss information from radiographs, and 3) build a prediction model to identify early bone loss patterns from radiographs before disease initiation and progression. Enhanced and consistent radiographs will improve diagnostic accuracy & reduce radiographic exposure, automatic bone loss measurement will reduce diagnostic discrepancies, and early bone loss detection will identify high-risk patients to take preventive approaches. The candidate, Dr. Patel's goal is to become an independent PI in dental informatics and develop cutting-edge technologies to generate practice- based evidence (using data-driven methods) to improve patient care and outcomes. A funded K08 proposal will allow Dr. Patel to develop the skills necessary to complete the proposed research (training in computer vision & radiology) and become an independent research scientist (training in didactic mentoring, lecturing, & grantsmanship). Dr. Patel has formed a team of five mentors with expertise in clinical dentistry, computer vision, radiology, and periodontology to provide high-quality, diverse scientific, collegial support and state-of-the-art facilities to ensure the successful completion of this proposed career development goals and research program.

摘要：牙周炎是第二大最常见但可预防的牙病，影响着多万人。 美国人对牙齿脱落、功能受限、疼痛和生活质量低下负有责任。因此，早些时候 在临床实践中，诊断和预防治疗势在必行，以防止疾病的发生和发展 进步。然而，当牙医可以观察到X光片中的第一批骨丢失模式来诊断时 牙周炎，30%-50%的恶化(牙周骨损伤)已经发生，这对 人类的眼睛。临床决策支持系统旨在识别高危牙周炎患者 预防；然而，由于次优预测，它们在临床实践中没有被广泛应用。 表现和缺乏多样化的预测特征(早期骨丢失损害)用于预测。因此，有 对一种工具的未得到满足的需求，该工具可以检测到人眼看不见的早期骨丢失模式，以提醒牙医 早期诊断和预防性护理。帕特尔博士开发了一种人工智能(AI)增强的预测 牙周炎模型使用了150多个不同的变量(例如，健康的社会决定因素、医学 记录、实验室报告、疾控中心人口普查数据、财务数据等)用于预测，这在 现有的文献。然而，该模型缺乏骨骼模式、骨密度、像素等牙科影像数据 强度和其他成像预测特征，这些特征具有提高预测精度的高潜力。这个 生物学研究已经研究了用于早期诊断的牙槽骨中早期骨矿物质的变化；然而， 这些调查结果在主席方面的过渡是有限的。人工智能和计算机视觉可以弥合这一差距并帮助 从人眼看不见的X光片中识别早期骨丢失模式。因此，这个项目的目标是 是开发三种自动化的计算机视觉算法：1)提高诊断性提取 来自根尖周围X线片的有意义的信息，2)以确定骨丢失的程度 X线片，以及3)建立预测模型以从疾病前的X线片中识别早期骨丢失模式 开始和进展。增强和一致的X光照片将提高诊断准确率并降低 X光照射，自动骨丢失测量将减少诊断的差异，和早期骨 丢失检测将识别高危患者，以便采取预防措施。候选人，帕特尔博士的目标是 成为牙科信息学的独立PI，并开发尖端技术来产生实践- 基于证据(使用数据驱动的方法)，以改善患者护理和结果。资助的K08提案将 允许Patel博士培养完成拟议研究所需的技能(计算机视觉和 放射学)，并成为一名独立的研究科学家(接受教学指导、讲课和 资质)。帕特尔博士组建了一个由五名导师组成的团队，他们在临床牙科、计算机视觉、 放射学和牙周病学，提供高质量、多样化的科学、大学支持和最先进的 确保顺利完成这项拟议的职业发展目标和研究计划的设施。