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OralCam: Enabling Self-Examination and Awareness of Oral Health Using a Smartphone Camera
arXiv - CS - Human-Computer Interaction Pub Date : 2020-01-16 , DOI: arxiv-2001.05621
Yuan Liang, Hsuan-Wei Fan, Zhujun Fang, Leiying Miao, Wen Li, Xuan Zhang, Weibin Sun, Kun Wang, Lei He, Xiang Anthony Chen

Due to a lack of medical resources or oral health awareness, oral diseases are often left unexamined and untreated, affecting a large population worldwide. With the advent of low-cost, sensor-equipped smartphones, mobile apps offer a promising possibility for promoting oral health. However, to the best of our knowledge, no mobile health (mHealth) solutions can directly support a user to self-examine their oral health condition. This paper presents OralCam, the first interactive app that enables end-users' self-examination of five common oral conditions (diseases or early disease signals) by taking smartphone photos of one's oral cavity. OralCam allows a user to annotate additional information (e.g. living habits, pain, and bleeding) to augment the input image, and presents the output hierarchically, probabilistically and with visual explanations to help a laymen user understand examination results. Developed on our in-house dataset that consists of 3,182 oral photos annotated by dental experts, our deep learning based framework achieved an average detection sensitivity of 0.787 over five conditions with high localization accuracy. In a week-long in-the-wild user study (N=18), most participants had no trouble using OralCam and interpreting the examination results. Two expert interviews further validate the feasibility of OralCam for promoting users' awareness of oral health.

中文翻译:

OralCam:使用智能手机摄像头实现自我检查和口腔健康意识

由于缺乏医疗资源或口腔健康意识,口腔疾病往往得不到检查和治疗,影响了全球大量人口。随着低成本、配备传感器的智能手机的出现,移动应用程序为促进口腔健康提供了有希望的可能性。然而,据我们所知,没有任何移动健康 (mHealth) 解决方案可以直接支持用户自我检查他们的口腔健康状况。本文介绍了 OralCam,这是第一个交互式应用程序,它使最终用户能够通过拍摄自己口腔的智能手机照片来自我检查五种常见口腔状况(疾病或早期疾病信号)。OralCam 允许用户注释附加信息(例如生活习惯、疼痛和出血)以增强输入图像,并分层呈现输出,概率和视觉解释,以帮助外行用户理解检查结果。我们的内部数据集由牙科专家注释的 3,182 张口腔照片组成,我们基于深度学习的框架在五种条件下实现了 0.787 的平均检测灵敏度,具有高定位精度。在为期一周的野外用户研究 (N=18) 中,大多数参与者在使用 OralCam 和解释检查结果方面没有问题。两位专家访谈进一步验证了 OralCam 在提升用户口腔健康意识方面的可行性。787 超过五种条件,定位精度高。在为期一周的野外用户研究 (N=18) 中,大多数参与者在使用 OralCam 和解释检查结果方面没有问题。两位专家访谈进一步验证了 OralCam 在提升用户口腔健康意识方面的可行性。787 超过五种条件,定位精度高。在为期一周的野外用户研究 (N=18) 中,大多数参与者在使用 OralCam 和解释检查结果方面没有问题。两位专家访谈进一步验证了 OralCam 在提升用户口腔健康意识方面的可行性。
更新日期:2020-01-24
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