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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要流行病学研究表明，牙齿破裂是牙齿脱落的第三大原因工业化国家。组织学研究表明，所有裂缝均被细菌定植，引起强烈疼痛的牙髓和根尖感染的潜力。裂缝的早期发现（不完整骨折）随后采取适当的干预措施以防止裂纹传播是防止的有效策略感染并避免牙齿脱落。当前用于诊断裂缝的工具不足，有必要需要开发一种客观可靠的方法来检测裂缝。在我们的阶段项目中，我们开发了并测试了一种新型算法，用于在提取的人牙齿上检测裂纹。使用机器学习和成像从三维（3D）小波中提取的特征，我们证明了增强的裂纹检测HR-CBCT。现在，我们建议进一步完善这项技术并在临床上进行验证。我们的假设是我们的方法增加了HR-CBCT在检测裂纹中的预测有效性。这种发展将在近距离临床上发生指导。此外，我们将与CBCT硬件供应商合作，以增加我们的商业化的影响计划。该提案解决了对检测定量，可重现和基于证据的方法的需求牙齿的裂缝可能会导致预防牙齿脱落的改善。