A pilot study on automated quantitative grading of facial functions

Gaber, Amira; Taher, Mona F.; Wahed, Manal Abdel

doi:10.21595/vp.2020.21283

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…[ 13 ] Some recent attempts to assess facial expression using specialised software include The Facial Action Coding System (FACS)[ 14 ] and Automated Facial Analysis using feature tracking (AFA). [ 15 16 17 ] They can provide information regarding maximal movement but cannot decipher deficits in facial expression. Secondary defects of facial nerve dysfunction have also not been uniformly incorporated into these systems.…”

Section: Discussionmentioning

confidence: 99%

“…To overcome these issues, many quantitative scales have been developed using techniques such as facial marker video analysis, digital facial motion analysis,[ 8 ] video pixel data using artificial neural networks[ 9 ] or automated quantitative grading of facial functions. [ 10 11 12 13 14 15 16 17 ] e-FACE[ 18 ] is one such popular software-based system for use on i-PAD to score static, dynamic function and disfigurement with slider scale for a total of 16 items in patients with facial palsy. The requirements of specific software, hardware, longer data entry time for these quantitative scales might limit widespread accessibility.…”

Section: Introductionmentioning

confidence: 99%

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Validation of a New Graphic Facial Nerve Grading System

Ojha¹,

Nagendra

Ansari

et al. 2022

Annals of Indian Academy of Neurology

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Background and Objective: Most of the existing qualitative facial nerve grading systems are very subjective while the quantitative grading systems are more complex, require longer data input time and specific software. There is a need for having a scoring system with graphic criteria to improve the subjectivity, reliability and convenience. We aimed to develop and validate such a reliable graphic scale for use in Bell's palsy. Methods: Face videos of patients with unilateral facial paralysis were recorded using smartphones and analyzed for six items including five voluntary facial movements apart from complications of facial palsy (synkinesis, hyperkinesis, and contracture). 15 videos were used for pilot study, 75 for the development of scale and 110 for its validation. Each video was rated on two separate occasions by 3 independent raters, a score of 0-4 was assigned to each item using the graphic scoring criteria, and a composite score was obtained (range 0–24). Five disease severity categories: normal (score 0), mild (score 1–6), moderate (score 7-12), severe (score: 13–18) and profound facial weakness (score: 19-24). Results: The proposed scale and its component items had high inter-rater and intra-rater reliability (Kappa >0.7). Good correlation (Pearson co-efficient >0.7) was seen among the voluntary movements. The proposed scale is a valid tool to score motor deficits and complications of facial palsy. Conclusions: The proposed scale is a valid and reliable graphic scale to describe facial motor dysfunction and its secondary defects.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of a New Graphic Facial Nerve Grading System

Ojha¹,

Nagendra

Ansari

et al. 2022

Annals of Indian Academy of Neurology

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“…The SDK 2.0 for the Kinect V2 includes a library for automatically acquiring 3D facial landmarks and Facial Animation Units (FAUs) which reflect the AUs. 3D facial landmarks from the Kinect sensor have been used previously in facial functions' assessment [24][25][26][27][28], and in FP evaluation [29]. FAUs from the Kinect sensor were previously used as a features for facial emotion and expression recognition [30][31][32].…”

Section: Feature Extractionmentioning

confidence: 99%

Classification of facial paralysis based on machine learning techniques

et al. 2022

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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.

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“…Landmarks and FAUs of the face have been used previously in facial movements' evaluation [16,17,[30][31][32][33] facial emotion and expression recognition [34][35][36], and in facial paralysis evaluation [37].…”

Section: Feature Extractionmentioning

confidence: 99%

Comprehensive assessment of facial paralysis based on facial animation units

Gaber

Taher²,

Wahed³

et al. 2022

PLoS ONE

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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.

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A pilot study on automated quantitative grading of facial functions

Cited by 6 publications

References 11 publications

Validation of a New Graphic Facial Nerve Grading System

Validation of a New Graphic Facial Nerve Grading System

Classification of facial paralysis based on machine learning techniques

Comprehensive assessment of facial paralysis based on facial animation units

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