基于智能手机的实时的幼儿龋病诊断系统
A Smartphone-based System for Real-time Early Childhood Caries Diagnosis
摘要：
幼儿龋齿（ECC）是6岁以下儿童中最常见但可预防的慢性疾病。严重ECC的治疗费用极其昂贵，社会经济弱势家庭负担不起。幼儿园的早期识别通常需要这方面的专业知识，因此往往被父母忽视。因此，需要早期预防策略和易于采用的诊断技术。在这项研究中，我们提出了一个多阶段的深度学习为基础的空洞检测系统。我们创建了一个包含牙科医生手动标记的RGB口腔图像的数据集。然后，我们研究了不同深度学习模型在数据集上的有效性。此外，我们将深度学习系统集成到一个易于使用的移动应用程序中，该应用程序可以从早期阶段诊断ECC，并为未经训练的用户提供实时的【形】结果。
英文摘要：
Early childhood caries (ECC) is the most common, yet preventable chronic disease in children under the age of 6. Treatments on severe ECC are extremely expensive and unaffordable for socioeconomically disadvantaged families. The identification of ECC in an early stage usually requires expertise in the field, and hence is often ignored by parents. Therefore, early prevention strategies and easy-to-adopt diagnosis techniques are desired. In this study, we propose a multistage deep learning-based system for cavity detection. We create a dataset containing RGB oral images labeled manually by dental practitioners. We then investigate the effectiveness of different deep learning models on the dataset. Furthermore, we integrate the deep learning system into an easy-to-use mobile application that can diagnose ECC from an early stage and provide real-time results to untrained users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
