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. The system is based on the Kinect v2 hardware and the accompanying software SDK 2.0 in extracting the real time facial landmarks and facial animation units (FAUs). The aim of this paper is to describe the development and testing of the FP assessment phase (first phase) of a larger comprehensive evaluation system of FP. The system includes two phases; FP assessment and FP classification. A dataset of 375 records from 13 unilateral FP patients was compiled for this study. The FP assessment includes three separate modules. One module is the symmetry assessment of both facial sides at rest and while performing five voluntary facial movements. Another module is responsible for recognizing the facial movements. The last module assesses the performance of each facial movement for both sides of the face depending on the involved FAUs. The study validates that the FAUs captured using the Kinect sensor can be processed and used to develop an effective tool for the automatic evaluation of FP. The developed FP grading system provides a detailed quantitative report and has significant advantages over the existing grading scales. It is fast, easy to use, user-independent, low cost, quantitative, and automated and hence it is suitable to be used as a clinical tool.

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. A comprehensive FP evaluation system has been developed by the authors. The system extracts real-time facial animation units (FAUs) using the Kinect V2 sensor and includes both FP assessment and classification. This paper describes the development and testing of the FP classification phase. A dataset of 375 records from 13 unilateral FP patients and 1650 records from 50 control subjects was compiled. Artificial Intelligence and Machine Learning methods are used to classify seven FP categories: the normal case and three severity levels: mild, moderate, and severe for the left and right sides. For better prediction results (Accuracy = 96.8%, Sensitivity = 88.9% and Specificity = 99%), an ensemble learning classifier was developed rather than one weak classifier. The ensemble approach based on SVMs was proposed for the high-dimensional data to gather the advantages of stacking and bagging. To address the problem of an imbalanced dataset, a hybrid strategy combining three separate techniques was used. Model robustness and stability was evaluated using fivefold cross-validation. The results showed that the classifier is robust, stable and performs well for different train and test samples. The study demonstrates that FAUs acquired by the Kinect sensor can be used in classifying FP. The developed FP assessment and classification system provides a detailed quantitative report and has significant advantages over existing grading scales.

In Vitro Induction of Apoptosis in Isolated Acute Myeloid Leukemia Cells: The Role of Anastatica hierochuntica Methanolic Extract.

Anastatica hierochuntica L. (Cruciferae) has been known in Egyptian folk medicine as a remedy for gastrointestinal disorders, diabetes and heart diseases. Despite the wide usage, A. hierochuntica research provides insufficient data to support its traditional practice. The cytotoxicity of A. hierochuntica methanolic extract was investigated on acute myeloid leukemia blasts (AML) and normal human peripheral leucocytes (NHPL). The phytochemical identification of bioactive compounds using 1H-NMR and LC-ESI-MS was also performed. A. hierochuntica extract caused non-significant cytotoxicity on NHPL, while the cytotoxicity on AML was significant (IC50: 0.38 ± 0.02 µg/mL). The negative expression of p53, upregulation of Caspase-3 and increase in the BAX/BCL-2 ratio were reported at the protein and mRNA levels. The results suggest that A. hierochuntica extract induced AML cell death via the p53-independent mitochondrial intrinsic pathway and further attention should be paid to this plant as a promising natural anticancer agent.

Effect of prenatal exposure of green tea extract on the developing central nervous system of rat fetuses; histological, immune-histochemical and ultrastructural studies.

Although, several health benefits were associated with green tea, these effects may be beneficial up to a certain dose. Higher doses of green tea may cause several adverse effects. So, there is a need to test the potential negative effects of green tea during pregnancy. This study was designated to evaluate the effect of prenatal exposure of green tea extract on the development of the central nervous system of 20-day old rat fetuses. The pregnant rats were divided into 4 groups; the control group (received distal water) and the other 3 groups received green tea extract at different doses (200, 600 & 1000 mg/kg/day, respectively) from the 6th to 15th day of gestation i.e., during the organogenesis phase of development. Cerebral cortex, cerebellum and spinal cord specimens were subjected to histological, immunohistochemical and ultrastructure investigations. The body weight of both mothers and fetuses was significantly decreased in the groups that received 600 and 1000 mg green tea extract. Also, the neuronal tissues displayed various signs of degeneration which were evident with the 600 and 1000 mg doses. Green tea extract also increases the glial fibrillary acidic protein (GFAP) and decreases the proliferating cell nuclear antigen (PCNA) which were directly proportional with increasing the dose. Administration of green tea extract during rat organogenesis period induced various histological, immunohistochemical and ultrastructural degenerative changes in the cerebral cortex, cerebellum and spinal cord of 20-day old rat fetuses. These deleterious changes were directly proportional to increasing the green tea extract dose. Thus, it should be stressed that the effect of green tea is dose-dependent and therefore it can be either beneficial or adverse.

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. These were used to compute the features used in a Support Vector Machine (SVM) classifier. A dataset of 1650 records from 50 normal subjects was compiled for this study. The performances of different SVM kernel models were tested with different feature groups. The best performance (Accuracy = 96.7%, Sensitivity = 90.2%, and Specificity = 98%) was found when using the RBF kernel model applied on just nine differences in FAUs. This research will be developed and extended to include FP classification.

Quantifying facial paralysis using the Kinect v2.

Assessment of facial paralysis (FP) and quantitative grading of facial asymmetry are essential in order to quantify the extent of the condition as well as to follow its improvement or progression. As such, there is a need for an accurate quantitative grading system that is easy to use, inexpensive and has minimal inter-observer variability. A comprehensive automated system to quantify and grade FP is the main objective of this work. An initial prototype has been presented by the authors. The present research aims to enhance the accuracy and robustness of one of this system's modules: the resting symmetry module. This is achieved by including several modifications to the computation method of the symmetry index (SI) for the eyebrows, eyes and mouth. These modifications are the gamma correction technique, the area of the eyes, and the slope of the mouth. The system was tested on normal subjects and showed promising results. The mean SI of the eyebrows decreased slightly from 98.42% to 98.04% using the modified method while the mean SI for the eyes and mouth increased from 96.93% to 99.63% and from 95.6% to 98.11% respectively while using the modified method. The system is easy to use, inexpensive, automated and fast, has no inter-observer variability and is thus well suited for clinical use.