Automated Dental Fracture Detection using High Resolution CBCT and Advanced Image Analysis

使用高分辨率 CBCT 和高级图像分析自动检测牙齿骨折

• 批准号: 10322271
• 负责人: Beatriz Paniagua
• 金额: $ 85.6万
• 依托单位: KITWARE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2023-09-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322271
• 关键词: 3-Dimensional; Address; Algorithms; Architecture; Bacteria; Clinical; Color; Computer software; Custom; Dental; Dental Cementum; Dental Enamel; Dentin; Dentistry; Detection; Developed Countries; Development; Diagnosis; Diagnostic; Dyes; Early Diagnosis; Elements; Endodontics; Equipment; Fracture; Gold; Human; Image; Image Analysis; Individual; Infection; Infection prevention; Intervention; Lead; Machine Learning; Maps; Methods; Methylene blue; Microscope; Morbidity - disease rate; Morphologic artifacts; Neurons; Operative Surgical Procedures; Outcome Measure; Pain; Participant; Patients; Phase; Predictive Value; Prevention; Probability; Public Health; Radio-Opaque; Recurrence; Reproducibility; Resolution; Scanning; Sensitivity and Specificity; Signs and Symptoms; Slice; Stains; Suggestion; Technology; Testing; Tooth Loss; Tooth structure; Training; Transillumination; Validation; Vendor; Visualization; X-Ray Computed Tomography; clinical decision-making; cohort; commercialization; cone-beam computed tomography; design; detection method; disability; effective intervention; epidemiology study; evidence base; feature extraction; graphical user interface; histological studies; improved; innovation; microCT; novel; periapical; prevent; primary outcome; programs; reconstruction; recruit; restoration; secondary outcome; segmentation algorithm; tool

PROJECT SUMMARY Epidemiological studies indicate that cracked teeth are the third most common cause of tooth loss in industrialized countries. Histological studies demonstrate that all cracks are colonized by bacteria, which have the potential to cause intensely painful pulpal and periapical infections. The early detection of cracks (incomplete fractures) followed by appropriate interventions to prevent crack propagation are effective strategies to prevent infections and avert tooth loss. Current tools used to diagnose cracks are inadequate and there is an imperative need to develop an objective and reliable method to detect cracks. During our Phase I project, we developed and tested a novel algorithm for crack detection on extracted human teeth. Using machine learning and imaging features extracted from three-dimensional (3D) wavelets, we demonstrated enhanced crack detection hr-CBCT. We now propose to further refine this technology and to validate it clinically. Our hypothesis is that our method increases the predictive validity of hr-CBCT in detecting cracks. This development will happen with close clinical guidance. Also, we will collaborate with CBCT hardware vendors to increase the impact of our commercialization plan. This proposal addresses the need for quantitative, reproducible, and evidence-based ways to detect cracks in teeth, that can potentially lead to improved tooth loss prevention.

项目总结 流行病学研究表明，隐裂牙齿是导致牙齿脱落的第三大常见原因 工业化国家。组织学研究表明，所有的裂缝都是由细菌定植的，细菌具有 有可能引起极度疼痛的牙髓和根尖周感染。早期发现裂缝(不完整 采取适当的干预措施以防止裂纹扩展是预防的有效策略 预防感染，避免牙齿脱落。目前用于诊断裂缝的工具是不够的，迫切需要 需要开发一种客观可靠的方法来检测裂缝。在我们的第一阶段项目中，我们开发了 并测试了一种对拔除的人类牙齿进行裂缝检测的新算法。使用机器学习和成像 从三维(3D)小波提取的特征，我们展示了增强的裂纹检测hr-CBCT。 我们现在建议进一步改进这项技术，并在临床上进行验证。我们的假设是我们的方法 提高了HR-CBCT在检测裂缝方面的预测有效性。这一发展将在密切的临床上发生 指导。此外，我们还将与CBCT硬件供应商合作，以增加我们商业化的影响 计划。这一建议满足了对定量、可重现和基于证据的检测方法的需求 牙齿上的裂缝，这可能会改善牙齿脱落的预防。