  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.
