Zhizhang Hu, Amir Radmehr, Yue Zhang, Shijia Pan, Phuc Nguyen
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引用次数: 0
摘要
咬合疾病--牙齿脱落的主要原因--严重影响患者的牙齿和健康,但却是目前最容易被忽视的牙科疾病。咬合疾病会导致进食困难、说话困难和长期头痛,最终影响患者的生活质量。虽然人们一直在尝试开发用于牙齿活动监测的传感系统,但目前还缺少能够为咬合监测提供足够传感分辨率的解决方案。为了填补这一空白,本文介绍了 IOTeeth,这是一种经济高效的自动口内传感系统,可对咬合疾病进行连续、精细的监测。IOTeeth 系统包括一个口内压电传感阵列,集成在一个牙科保持器平台上,支持可靠的咬合疾病识别。IOTeeth 主要监测犬齿和前牙的咬合和磨牙活动,这些活动包含咬合的基本信息。IOTeeth 的口内可穿戴设备收集来自传感器的信号,并将这些信号提取到一个名为 "物理感知注意力网络(PAN Net)"的轻量级鲁棒深度学习模型中,用于咬合疾病识别。我们使用牙科诊所患者的 12 个铰接牙齿模型对 IOTeeth 进行了评估。评估结果显示,在留空验证的情况下,活动识别的 F1 得分为 0.97,在留空验证的情况下,不同活动的牙科疾病识别平均 F1 得分为 0.92。
IOTeeth: Intra-Oral Teeth Sensing System for Dental Occlusal Diseases Recognition
While occlusal diseases - the main cause of tooth loss -- significantly impact patients' teeth and well-being, they are the most underdiagnosed dental diseases nowadays. Experiencing occlusal diseases could result in difficulties in eating, speaking, and chronicle headaches, ultimately impacting patients' quality of life. Although attempts have been made to develop sensing systems for teeth activity monitoring, solutions that support sufficient sensing resolution for occlusal monitoring are missing. To fill that gap, this paper presents IOTeeth, a cost-effective and automated intra-oral sensing system for continuous and fine-grained monitoring of occlusal diseases. The IOTeeth system includes an intra-oral piezoelectric-based sensing array integrated into a dental retainer platform to support reliable occlusal disease recognition. IOTeeth focuses on biting and grinding activities from the canines and front teeth, which contain essential information of occlusion. IOTeeth's intra-oral wearable collects signals from the sensors and fetches them into a lightweight and robust deep learning model called Physioaware Attention Network (PAN Net) for occlusal disease recognition. We evaluate IOTeeth with 12 articulator teeth models from dental clinic patients. Evaluation results show an F1 score of 0.97 for activity recognition with leave-one-out validation and an average F1 score of 0.92 for dental disease recognition for different activities with leave-one-out validation.