To produce a reliable objective method of assessing the House-Brackmann (H-B) and regional grades of facial palsy with the results produced and presented in a time and manner suitable for a routine clinical setting.Study Design:
Analysis of video pixel data using artificial neural networks (ANNs).Setting:
Tertiary-referral neuro-otologic center.Subjects:
Subjects with varying degrees of unilateral facial palsy.Method:
Clinicians assessed videos of subjects with varying degrees of facial palsy performing prescribed movements. The results of their overall and regional assessments were used to train ANNs. These were then tested for consistency, accuracy, and ability to identify clinical changes in grading.Results:
A group of subjects had their objective computer assessment repeated, and consistent H-B and regional grades were obtained. A second group had both subjective clinical and objective computer assessments performed. The program gave results that were within the expected level of agreement with the subjective clinical assessment for both H-B and regional grades. A third group had repeated clinical and computer assessments from the time of onset to recovery of facial function. The changes in the computer results both for H-B and regional grades tracked the clinical change.Conclusion:
It is possible to measure consistently and objectively the H-B and regional grades of facial palsy using trained ANNs to analysis video pixel data, and this can be done in a routine clinical environment by a technician. The results from each region of the face are presented as a Facogram along with the H-B grade.