Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Apr 15, 2020
Date Accepted: Nov 30, 2020
Utilization of Smartphones’ Depth Mapping Cameras for the App-based Grading of Facial Movement Disorders: Design and Development
ABSTRACT
Background:
For the classification of facial palsy, various efforts have been made for the description and evaluation of clinician-graded and software-based scoring systems. They serve the purpose of scientific and clinical assessment of the spontaneous course of the disease or monitoring therapeutic interventions. Nevertheless, none of them could achieve universal acceptance in everyday clinical practice. Hence, a quick and precise tool for assessing the functional status of the facial nerve would be desirable. In this context, the possibilities that depth mapping cameras of recent smartphones offer have sparked our interest.
Objective:
This study describes the utilization of a smartphone’s depth mapping camera via a specially developed app prototype for a quick, objective and reproducible quantification of facial asymmetries.
Methods:
After conceptual and user interface design a native app prototype for iOS was programmed, that accesses and processes the data of the TrueDepth-camera. Using a special algorithm, the app returns a new index for the grading of unilateral facial palsy ranging from 0% to 100%, called Digital Facial Index. The algorithm was adapted to the well-established Stennert’s index by weighting the individual facial regions based on functional and cosmetic aspects. Test measurements were performed in order to proof the reliability of the system.
Results:
The app prototype turned out to be stable and met all of the criteria previously defined. The newly defined index expresses the results of the measurement as an easily understandable numerical value for each half of the face. Test measurements were reproducible and revealed no statistically significant intertest variability.
Conclusions:
The use of a smartphone’s depth mapping camera has considerable potential for the app-based grading of facial movement disorders. The app and its algorithm, which is based on theoretical considerations, should be checked in a prospective clinical study and correlated with common facial scores.
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