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：
10491799
负责人：
Beatriz Paniagua
金额：
$ 84.43万
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 2024-09-21
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10491799
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.

项目概要流行病学研究表明，牙齿隐裂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，所有裂缝都被细菌定殖，这些细菌可能引起剧烈疼痛的牙髓和根尖周感染。及早发现裂纹（不完全裂缝），然后采取适当的干预措施以防止裂纹扩展，是防止裂纹扩展的有效策略感染并避免牙齿脱落。目前用于诊断裂纹的工具还不够，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用于拔除人类牙齿裂纹检测的新算法。使用机器学习和成像利用从三维 (3D) 小波中提取的特征，我们演示了增强型裂纹检测 hr-CBCT。我们现在建议进一步完善这项技术并进行临床验证。我们的假设是我们的方法提高了 hr-CBCT 在检测裂纹时的预测有效性。这一发展将通过密切的临床研究来实现指导。此外，我们将与 CBCT 硬件供应商合作，以提高我们商业化的影响力计划。该提案解决了对定量、可重复和基于证据的方法来检测的需求牙齿裂纹，这可能会改善牙齿脱落的预防。