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.

翻译后摘要：牙周炎是第二个最流行的，但可预防的牙科疾病影响超过64万 美国人和负责牙齿脱落，功能限制，疼痛和生活质量差。因此，早期 诊断和预防性治疗在临床实践中是必要的，以防止疾病的发生， 进展然而，当牙科医生能够在X光片中观察到第一个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，这是不可见的， 人类的眼睛临床决策支持系统的目的是识别高危牙周炎患者， 预防;然而，由于次优预测，它们未在临床实践中广泛使用 性能和缺乏不同的预测特征（早期骨丢失病变）进行预测。因此有 对能够检测人眼不可见的早期骨丢失模式以提醒牙医 早期诊断和预防护理。帕特尔博士开发了一种人工智能（AI）预测 使用超过150个不同变量的牙周炎模型（例如，健康的社会决定因素，医疗 记录、实验室报告、CDC普查数据、财务数据等）预测的能力，这一点在 现有文献。然而，该模型缺乏牙齿成像数据，诸如骨图案、骨密度、像素、牙齿形状、牙齿 强度和其他成像预测特征，其具有提高预测准确性的高潜力。的 在生物学研究中已经研究了用于早期诊断的牙槽骨中的早期骨矿物质变化;然而， 这些调查结果在主席身边的传递是有限的。AI和计算机视觉可以弥合这一差距， 从人眼看不见的射线照片中识别早期骨丢失模式。因此，本项目的目标 是开发三个自动化的计算机视觉算法：1）提高诊断提取 从根尖X线片中获得有意义的信息，2）确定骨丢失信息的程度， 建立预测模型，以从疾病前的X光片中识别早期骨丢失模式 启动和进展。增强和一致的X光片将提高诊断准确性并减少 放射线曝光，自动骨丢失测量将减少诊断差异， 失检将识别高危患者，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，并开发尖端技术以产生实践- 基于证据（使用数据驱动的方法），以改善患者护理和结果。K 08提案将获得资助 允许Patel博士发展完成拟议研究所需的技能（计算机视觉培训和 放射学），并成为一个独立的研究科学家（在教学指导，讲课， granite）。Patel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，学院支持和国家的最先进的 设施，以确保成功完成本建议的职业发展目标和研究计划。