Comprehensive assessment of facial paralysis based on facial animation units.

Quantitative grading and classification of the severity of facial paralysis (FP) are important for selecting the treatment plan and detecting subtle improvement that cannot be detected clinically. To date, none of the available FP grading systems have gained widespread clinical acceptance. The work presented here describes the development and testing of a system for FP grading and assessment which is part of a comprehensive evaluation system for FP.

Classification of facial paralysis based on machine learning techniques.

Facial paralysis (FP) is an inability to move facial muscles voluntarily, affecting daily activities. There is a need for quantitative assessment and severity level classification of FP to evaluate the condition. None of the available tools are widely accepted.

SVM classification of facial functions based on facial landmarks and animation Units.

Quantitative assessment and classification of facial paralysis (FP) are essential for treatment selection and progress evaluation of the condition. As part of a comprehensive framework towards this goal, this study aims to classify five normal facial functions: smiling, eye closure, raising the eyebrows, blowing cheeks, and whistling as well as the rest state. 3D facial landmarks and facial animation units (FAUs) were obtained using the Kinect V2, a fast and cost-effective depth camera.

Could SARS-CoV-2 herald a surge of multiple sclerosis?

During the current COVID-19 pandemic, many queries are raised regarding its nature, outcome, and sequelae. This letter raises the concern of potential impact on increasing the incidence of multiple sclerosis whose pathology involves a possible viral etiology. Besides, the potential neurotropism of the acute respiratory distress syndrome corona virus-2 (SARS-CoV-2), which is still not established, may raise concerns about the use of certain disease modifying therapies namely natalizumab.