Toward automated severe pharyngitis detection with smartphone camera using deep learning networks

Here we present a deep learning model with smartphone-based throat images facilitating detection of severe pharyngitis in telemedicine settings.

We collected throat images from the web-based open social Q&A systems including Naver Korea (https://kin.naver.com), Yahoo Japan (https://chiebukuro.yahoo.co.jp). The additional throat image datasets were extracted using the Google image search engine. The search strategy was based on the key terms “sore throat”, “pharyngitis”, “tonsillitis”, “exudative tonsillitis”, “tonsillopharyngitis”, “throat image”, and “smartphone” in Korean, Japanese, and English. The most updated electronic database search was on June 30, 2020. We manually excluded throat images which were not acquired using smartphone. The images with the characteristics of the pharyngitis were manually classified by two clinicians, and the ambiguous images were isolated to clarify the image domains. Finally, we collected the initial dataset with a total of two classes including 147 throat images with pharyngitis and 215 normal throat images.

本项研究呈现了一种基于智能手机喉部图像的深度学习模型，该模型有助于在远程医疗环境下对严重咽喉炎进行检测。我们收集了来自网络开放式社交问答系统，包括Naver Korea（https://kin.naver.com）和Yahoo Japan（https://chiebukuro.yahoo.co.jp）的喉部图像。此外，我们还利用Google图像搜索引擎提取了额外的喉部图像数据集。搜索策略基于“喉咙痛”、“咽喉炎”、“扁桃体炎”、“渗出性扁桃体炎”、“扁桃体咽炎”、“喉部图像”和“智能手机”等关键词，分别用韩语、日语和英语进行搜索。截至2020年6月30日，我们对最新的电子数据库进行了搜索。我们手动排除了非智能手机拍摄的喉部图像。具有咽喉炎特征的图像由两位临床医生进行手动分类，模糊图像则被隔离以明确图像领域。最终，我们收集了包含147张咽喉炎喉部图像和215张正常喉部图像的初始数据集，共计两个类别。