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

项目摘要 流行病学研究表明，牙隐裂是第三个最常见的原因，牙齿脱落， 工业化国家。组织学研究表明，所有裂缝都被细菌定植， 可能引起剧烈疼痛的牙髓和根尖周感染。裂缝的早期检测（不完整 裂缝），然后进行适当的干预，以防止裂纹扩展是有效的策略，以防止 感染和避免牙齿脱落。目前用于诊断裂缝的工具是不够的， 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 并测试了一种新的算法，用于在提取的人类牙齿上进行裂缝检测。使用机器学习和成像 特征提取的三维（3D）小波，我们证明了增强的裂纹检测hr-CBCT。 我们现在建议进一步完善这项技术，并在临床上验证它。我们的假设是我们的方法 提高了hr-CBCT检测裂纹的预测有效性。这种发展将发生在密切的临床 指导此外，我们将与CBCT硬件供应商合作，以增加我们商业化的影响力 计划该提案解决了定量，可重复和基于证据的方法来检测的需求 牙齿的裂缝，这可能会导致改善牙齿脱落的预防。

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.