Inspiration

The statistics shows that the current traditional methods of dental care spend on diagnosing more than the treatment itself. Moreover, many patients tend to get frustrated of waiting in the clinic itself for more than 15-20 minutes. Therefore, the digitalized approach must be implemented in order to tackle those problems. The concept of virtual clinic for patient that we came up with not only solves the issues above but also

What it does

Our project solves the problem with diagnosing in a special app where the medical history of a patient is taken virtually. In addition to that, the advanced technologies of 3D-imaging devices are used for VR representation of the patient's full-size image of a scalp.

The app also provides a user-friendly interface with some additional functions such as treatment stage tracking, digital recommendations with notifications added, and easy access to a clinic. In addition to that, our app uses proximi.io iBeacon devices in order to help the user to navigate himself in the clinic.

How we built it

We've built a prototype of an app using Android Studio 3.0 and SDK tools implementing public available APIs. Firebase is used for authentication, Remote database, Rxjava was used to adopt asynchronous REST queries. Proximi.io APIs were used to integrate iBeacon solution.

Challenges we ran into

The VR representation is not well-developed because the models were to big to open on mobile devices because of lack of the resources. Also, the

Accomplishments that we're proud of

The successful collaboration of medical technologies with IT.

What we learned

3D imaging devices in digitalized world and indoor navigation via iBeacon

What's next for Dentify

Currently, we have to improve the following difficulties we ran into: -We should improve our VR representation in terms of -We can improve the navigation not only through iBeacons but also via open WiFi networks in order to increase precision -We can also improve the quality of service through using the data obtained from VR Oculus and EEG for maintaining the patient anxiety in the app.

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