Objective grading facial paralysis severity using a dynamic 3D stereo photogrammetry imaging system. (March 2022)

• Record Type: Journal Article
• Authors: Alagha, Mahmoud A; Ayoub, Ashraf; Morley, Stephen; Ju, Xiangyang
• Is Part Of: Optics and lasers in engineering. Volume 150(2022)
• Publication Date: 2022-03
• Subjects: Facial paralysis -- Dynamic 3D imaging -- Facial asymmetry -- Machine learning -- Correlation; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2021.106876
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: A comprehensive review of subjective and objective facial paralysis grading. Investigate the correlations between the objective asymmetry scores calculated from dynamics images and the grades of a modified Sunnybrook grading system. Machine learning approaches applied which showed a potential to improve the accuracy of objective facial paralysis grading. Abstract: Facial paralysis is a loss of facial movement due to nerve damage. It is essential for clinicians to diagnose the severity of the facial paralysis to treat patients, assess progresses and evaluate outcomes. Subjective assessments are common in clinical practices but have their limitations regarding the intra-observer and inter-observer reproducibility. We utilised the dynamic 3D stereo photogrammetry technology for the objective grading of facial paralysis by measuring regional facial asymmetries. The correlations between the measured asymmetries and scores of a modified Sunnybrook facial paralysis grading were evaluated to identify the region of interests of objective measurements closely related to the subjective grades. Categorical classifiers were trained to quantify the severity of the facial paralysis. Preliminary results showed that the objective asymmetry measurements were highly correlated to the subjective assessments of facial paralysis except the eye region. Machine learning approaches showed a potential of improving the accuracy of severity assessments.