论文标题
一种基于智能手机的实时幼儿龋齿诊断的系统
A Smartphone-based System for Real-time Early Childhood Caries Diagnosis
论文作者
论文摘要
儿童龋齿（ECC）是6岁以下儿童中最常见的，但可预防的慢性疾病。严重的ECC治疗非常昂贵且对社会经济处境不利的家庭负担不起。在早期阶段对ECC的识别通常需要该领域的专业知识，因此父母常常会忽略。因此，需要早期的预防策略和易于使用的诊断技术。在这项研究中，我们提出了一个基于多阶段的深度学习系统，用于腔检测。我们创建一个包含牙科医生手动标记的RGB口头图像的数据集。然后，我们研究数据集上不同深度学习模型的有效性。此外，我们将深度学习系统集成到易于使用的移动应用程序中，该应用程序可以从早期诊断ECC，并为未经培训的用户提供实时结果。
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.
