The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Kenneth L. Tyler - One of the best experts on this subject based on the ideXlab platform.
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Bell's palsy--is glucocorticoid treatment enough?
The New England Journal of Medicine, 2007Co-Authors: Donald H Gilden, Kenneth L. TylerAbstract:Approximately a third of cases of acute peripheral Facial Weakness are caused by trauma, diabetes mellitus, hypertension, eclampsia, the Ramsay Hunt syndrome (Facial palsy with zoster oticus caused by varicella–zoster virus), Lyme disease, sarcoidosis, Sjogren's syndrome, parotid gland tumors, and amyloidosis and may even be a complication of intranasal influenza vaccine.1 The remaining two thirds of cases are idiopathic (Bell's palsy). Bell's palsy occurs in 20 to 32 persons per 100,000 per year2,3 and affects both sexes and all ages. Fortunately, most patients with Bell's palsy recover completely, but 20 to 30% may have permanent, disfiguring Facial Weakness or . . .
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One and one-half syndrome with supranuclear Facial Weakness: magnetic resonance imaging localization.
JAMA Neurology, 1999Co-Authors: C. Alan Anderson, Elliot Sandberg, Sally L. Harris, Christopher M. Filley, Kenneth L. TylerAbstract:Objective To provide clinicoanatomical correlation for a small pontine tegmental ischemic stroke producing the one and one-half syndrome associated with supranuclear Facial Weakness. Design Case report. Setting Tertiary care center. Patient A 70-year-old man developed left-sided Facial Weakness sparing the forehead, a left internuclear ophthalmoplegia, and a complete left horizontal gaze palsy immediately after percutaneous transluminal coronary angioplasty. Magnetic resonance imaging demonstrated a small lesion in the left paramedian aspect of the dorsal pontine tegmentum. Main Outcome and Results Electromyographic findings were consistent with supranuclear Facial involvement. The patient had nearly complete recovery after 1 year. Conclusions To our knowledge, this is the first report of supranuclear Facial Weakness in association with the one and one-half syndrome. The location of the lesion provides evidence of the existence of corticofugal fibers that extend to the Facial nucleus in the dorsal paramedian pontine tegmentum.
Gustavo K Rohde - One of the best experts on this subject based on the ideXlab platform.
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video based Facial Weakness analysis
IEEE Transactions on Biomedical Engineering, 2021Co-Authors: Yan Zhuang, Mark Mcdonald, Chad Aldridge, Mohamed Abul Hassan, Omar Uribe, Daniel Arteaga, Andrew M Southerland, Gustavo K RohdeAbstract:Objective: Facial Weakness is a common sign of neurological diseases such as Bell's palsy and stroke. However, recognizing Facial Weakness still remains as a challenge, because it requires experience and neurological training. Methods: We propose a framework for Facial Weakness detection, which models the temporal dynamics of both shape and appearance-based features of each target frame through a bi-directional long short-term memory network (Bi-LSTM). The system is evaluated on a in-the-wild video dataset that is verified by three board-certified neurologists. In addition, three emergency medical services (EMS) personnel and three upper level residents rated the dataset. We compare the evaluation of the proposed algorithm with other comparison methods as well as the human raters. Results: Experimental evaluation demonstrates that: (1) the proposed algorithm achieves the accuracy, sensitivity, and specificity of 94.3%, 91.4%, and 95.7%, which outperforms other comparison methods and achieves the equal performance to paramedics; (2) the framework can provide visualizable and interpretable results that increases model transparency and interpretability; (3) a prototype is implemented as a proof-of-concept showcase to show the feasibility of an inexpensive solution for Facial Weakness detection. Conclusion: The experiment results suggest that the proposed framework can identify Facial Weakness effectively. Significance: We provide a proof-of-concept study, showing that such technology could be used by non-neurologists to more readily identify Facial Weakness in the field, leading to increasing coverage and earlier treatment.
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Facial Weakness Analysis and Quantification of Static Images
IEEE Journal of Biomedical and Health Informatics, 2020Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Andrew M Southerland, Dhyey Parikh, Gustavo K RohdeAbstract:Facial Weakness is a symptom commonly associated to lack of Facial muscle control due to neurological injury. Several diseases are associated with Facial Weakness such as stroke and Bell's palsy. The use of digital imaging through mobile phones, tablets, personal computers and other devices could provide timely opportunity for detection, which if accurate enough can improve treatment by enabling faster patient triage and recovery progress monitoring. Most of the existing Facial Weakness detection approaches from static images are based on Facial landmarks from which geometric features can be calculated. Landmark-based methods, however, can suffer from inaccuracies in face landmarks localization. In this study, We also experimentally evaluate the performance of several feature extraction methods for measuring Facial Weakness, including the landmark-based features, as well as intensity-based features on a neurologist-certified dataset that comprises 186 images of normal, 125 images of left Facial Weakness, and 126 images of right Facial Weakness. We demonstrate that, for the application of Facial Weakness detection from single (static) images, approaches that incorporate the Histogram of Oriented Gradients (HoG) features tend to be more accurate.
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f dit v an automated video classification tool for Facial Weakness detection
IEEE-EMBS International Conference on Biomedical and Health Informatics, 2019Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Andrew M Southerland, Dhyey Parikh, Xuwang Yin, Gustavo K RohdeAbstract:Facial Weakness is a common presenting sign of several neurological diseases including stroke, traumatic brain injury (TBI), and Bell's palsy. Tools to improve the accuracy of Facial Weakness detection can prompt quicker evaluation into these diseases possibly resulting in earlier diagnoses. In this study, we propose an automated video classification detection tool, Facial Deficit Identification Tool for Videos (F-DIT-V), for Facial Weakness detection. This tool exploits Histogram of Oriented Gradients (HOG) features to perform more accurate Facial Weakness detection for a given video. Using experimental data we demonstrate that F-DIT-V achieves a classification accuracy of 92.9%, precision of 93.6%, recall of 92.8%, and specificity of 94.2%. F-DIT-V is able to achieve higher and more reliable performance compared to existing (e.g. LBP-TOP, RNN-based) methods which are widely used in previous and current studies for Facial Weakness video classification. As the proposed camera-based analysis system requires no extra hardware, F-DIT-V could be implemented in a low-cost, portable, and easy to use format for generalizability to real world settings.
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abstract tp274 comparison of human and machine learning based Facial Weakness detection
Stroke, 2019Co-Authors: Mark Mcdonald, Yan Zhuang, Omar Uribe, Gustavo K Rohde, Iris Lin, Haydon Pitchford, William A Darlymple, Andrew M SoutherlandAbstract:Background: Prehospital stroke screening tools are essential to rapid diagnosis and treatment, but variable recognition of common stroke deficits by EMS providers limits the accuracy and reliabilit...
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pathological Facial Weakness detection using computational image analysis
International Symposium on Biomedical Imaging, 2018Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Daniel Arteaga, Andrew M Southerland, Iris Lin, William Dalrymple, Bradford B Worrall, Gustavo K RohdeAbstract:Stroke is the leading cause of disability in the U.S. Early recognition and treatment of stroke decreases the mortality and possibility of severe injury. One prominent stroke symptom is Facial Weakness. Existing methods such as telestroke systems, which require complex configuration including a tablet, a dedicated video conference system, 4G wireless connection, and a standby clinical support team for diagnosis, are far from widely practical use. In this pilot study, we present an automatic pathological Facial Weakness detection tool based on computational image analysis. The proposed system is able to extract the Facial landmarks and classify Facial Weakness using a learning method. In the experiment, each image in the dataset is scored independently by two senior neurology residents. Only images rated concordantly by both raters as likely normal or likely abnormal (including left Facial Weakness and right Facial Weakness) were included for analysis. Our method performed with overall accuracy of 94.5%, precision of 94.8%, sensitivity of 94.6%, and specificity of 96.8%. The experimental result indicates that our method is able to identify pathological Facial Weakness accurately on the static images.
Andrew M Southerland - One of the best experts on this subject based on the ideXlab platform.
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video based Facial Weakness analysis
IEEE Transactions on Biomedical Engineering, 2021Co-Authors: Yan Zhuang, Mark Mcdonald, Chad Aldridge, Mohamed Abul Hassan, Omar Uribe, Daniel Arteaga, Andrew M Southerland, Gustavo K RohdeAbstract:Objective: Facial Weakness is a common sign of neurological diseases such as Bell's palsy and stroke. However, recognizing Facial Weakness still remains as a challenge, because it requires experience and neurological training. Methods: We propose a framework for Facial Weakness detection, which models the temporal dynamics of both shape and appearance-based features of each target frame through a bi-directional long short-term memory network (Bi-LSTM). The system is evaluated on a in-the-wild video dataset that is verified by three board-certified neurologists. In addition, three emergency medical services (EMS) personnel and three upper level residents rated the dataset. We compare the evaluation of the proposed algorithm with other comparison methods as well as the human raters. Results: Experimental evaluation demonstrates that: (1) the proposed algorithm achieves the accuracy, sensitivity, and specificity of 94.3%, 91.4%, and 95.7%, which outperforms other comparison methods and achieves the equal performance to paramedics; (2) the framework can provide visualizable and interpretable results that increases model transparency and interpretability; (3) a prototype is implemented as a proof-of-concept showcase to show the feasibility of an inexpensive solution for Facial Weakness detection. Conclusion: The experiment results suggest that the proposed framework can identify Facial Weakness effectively. Significance: We provide a proof-of-concept study, showing that such technology could be used by non-neurologists to more readily identify Facial Weakness in the field, leading to increasing coverage and earlier treatment.
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Facial Weakness Analysis and Quantification of Static Images
IEEE Journal of Biomedical and Health Informatics, 2020Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Andrew M Southerland, Dhyey Parikh, Gustavo K RohdeAbstract:Facial Weakness is a symptom commonly associated to lack of Facial muscle control due to neurological injury. Several diseases are associated with Facial Weakness such as stroke and Bell's palsy. The use of digital imaging through mobile phones, tablets, personal computers and other devices could provide timely opportunity for detection, which if accurate enough can improve treatment by enabling faster patient triage and recovery progress monitoring. Most of the existing Facial Weakness detection approaches from static images are based on Facial landmarks from which geometric features can be calculated. Landmark-based methods, however, can suffer from inaccuracies in face landmarks localization. In this study, We also experimentally evaluate the performance of several feature extraction methods for measuring Facial Weakness, including the landmark-based features, as well as intensity-based features on a neurologist-certified dataset that comprises 186 images of normal, 125 images of left Facial Weakness, and 126 images of right Facial Weakness. We demonstrate that, for the application of Facial Weakness detection from single (static) images, approaches that incorporate the Histogram of Oriented Gradients (HoG) features tend to be more accurate.
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f dit v an automated video classification tool for Facial Weakness detection
IEEE-EMBS International Conference on Biomedical and Health Informatics, 2019Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Andrew M Southerland, Dhyey Parikh, Xuwang Yin, Gustavo K RohdeAbstract:Facial Weakness is a common presenting sign of several neurological diseases including stroke, traumatic brain injury (TBI), and Bell's palsy. Tools to improve the accuracy of Facial Weakness detection can prompt quicker evaluation into these diseases possibly resulting in earlier diagnoses. In this study, we propose an automated video classification detection tool, Facial Deficit Identification Tool for Videos (F-DIT-V), for Facial Weakness detection. This tool exploits Histogram of Oriented Gradients (HOG) features to perform more accurate Facial Weakness detection for a given video. Using experimental data we demonstrate that F-DIT-V achieves a classification accuracy of 92.9%, precision of 93.6%, recall of 92.8%, and specificity of 94.2%. F-DIT-V is able to achieve higher and more reliable performance compared to existing (e.g. LBP-TOP, RNN-based) methods which are widely used in previous and current studies for Facial Weakness video classification. As the proposed camera-based analysis system requires no extra hardware, F-DIT-V could be implemented in a low-cost, portable, and easy to use format for generalizability to real world settings.
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abstract tp274 comparison of human and machine learning based Facial Weakness detection
Stroke, 2019Co-Authors: Mark Mcdonald, Yan Zhuang, Omar Uribe, Gustavo K Rohde, Iris Lin, Haydon Pitchford, William A Darlymple, Andrew M SoutherlandAbstract:Background: Prehospital stroke screening tools are essential to rapid diagnosis and treatment, but variable recognition of common stroke deficits by EMS providers limits the accuracy and reliabilit...
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pathological Facial Weakness detection using computational image analysis
International Symposium on Biomedical Imaging, 2018Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Daniel Arteaga, Andrew M Southerland, Iris Lin, William Dalrymple, Bradford B Worrall, Gustavo K RohdeAbstract:Stroke is the leading cause of disability in the U.S. Early recognition and treatment of stroke decreases the mortality and possibility of severe injury. One prominent stroke symptom is Facial Weakness. Existing methods such as telestroke systems, which require complex configuration including a tablet, a dedicated video conference system, 4G wireless connection, and a standby clinical support team for diagnosis, are far from widely practical use. In this pilot study, we present an automatic pathological Facial Weakness detection tool based on computational image analysis. The proposed system is able to extract the Facial landmarks and classify Facial Weakness using a learning method. In the experiment, each image in the dataset is scored independently by two senior neurology residents. Only images rated concordantly by both raters as likely normal or likely abnormal (including left Facial Weakness and right Facial Weakness) were included for analysis. Our method performed with overall accuracy of 94.5%, precision of 94.8%, sensitivity of 94.6%, and specificity of 96.8%. The experimental result indicates that our method is able to identify pathological Facial Weakness accurately on the static images.
Yan Zhuang - One of the best experts on this subject based on the ideXlab platform.
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video based Facial Weakness analysis
IEEE Transactions on Biomedical Engineering, 2021Co-Authors: Yan Zhuang, Mark Mcdonald, Chad Aldridge, Mohamed Abul Hassan, Omar Uribe, Daniel Arteaga, Andrew M Southerland, Gustavo K RohdeAbstract:Objective: Facial Weakness is a common sign of neurological diseases such as Bell's palsy and stroke. However, recognizing Facial Weakness still remains as a challenge, because it requires experience and neurological training. Methods: We propose a framework for Facial Weakness detection, which models the temporal dynamics of both shape and appearance-based features of each target frame through a bi-directional long short-term memory network (Bi-LSTM). The system is evaluated on a in-the-wild video dataset that is verified by three board-certified neurologists. In addition, three emergency medical services (EMS) personnel and three upper level residents rated the dataset. We compare the evaluation of the proposed algorithm with other comparison methods as well as the human raters. Results: Experimental evaluation demonstrates that: (1) the proposed algorithm achieves the accuracy, sensitivity, and specificity of 94.3%, 91.4%, and 95.7%, which outperforms other comparison methods and achieves the equal performance to paramedics; (2) the framework can provide visualizable and interpretable results that increases model transparency and interpretability; (3) a prototype is implemented as a proof-of-concept showcase to show the feasibility of an inexpensive solution for Facial Weakness detection. Conclusion: The experiment results suggest that the proposed framework can identify Facial Weakness effectively. Significance: We provide a proof-of-concept study, showing that such technology could be used by non-neurologists to more readily identify Facial Weakness in the field, leading to increasing coverage and earlier treatment.
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Facial Weakness Analysis and Quantification of Static Images
IEEE Journal of Biomedical and Health Informatics, 2020Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Andrew M Southerland, Dhyey Parikh, Gustavo K RohdeAbstract:Facial Weakness is a symptom commonly associated to lack of Facial muscle control due to neurological injury. Several diseases are associated with Facial Weakness such as stroke and Bell's palsy. The use of digital imaging through mobile phones, tablets, personal computers and other devices could provide timely opportunity for detection, which if accurate enough can improve treatment by enabling faster patient triage and recovery progress monitoring. Most of the existing Facial Weakness detection approaches from static images are based on Facial landmarks from which geometric features can be calculated. Landmark-based methods, however, can suffer from inaccuracies in face landmarks localization. In this study, We also experimentally evaluate the performance of several feature extraction methods for measuring Facial Weakness, including the landmark-based features, as well as intensity-based features on a neurologist-certified dataset that comprises 186 images of normal, 125 images of left Facial Weakness, and 126 images of right Facial Weakness. We demonstrate that, for the application of Facial Weakness detection from single (static) images, approaches that incorporate the Histogram of Oriented Gradients (HoG) features tend to be more accurate.
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f dit v an automated video classification tool for Facial Weakness detection
IEEE-EMBS International Conference on Biomedical and Health Informatics, 2019Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Andrew M Southerland, Dhyey Parikh, Xuwang Yin, Gustavo K RohdeAbstract:Facial Weakness is a common presenting sign of several neurological diseases including stroke, traumatic brain injury (TBI), and Bell's palsy. Tools to improve the accuracy of Facial Weakness detection can prompt quicker evaluation into these diseases possibly resulting in earlier diagnoses. In this study, we propose an automated video classification detection tool, Facial Deficit Identification Tool for Videos (F-DIT-V), for Facial Weakness detection. This tool exploits Histogram of Oriented Gradients (HOG) features to perform more accurate Facial Weakness detection for a given video. Using experimental data we demonstrate that F-DIT-V achieves a classification accuracy of 92.9%, precision of 93.6%, recall of 92.8%, and specificity of 94.2%. F-DIT-V is able to achieve higher and more reliable performance compared to existing (e.g. LBP-TOP, RNN-based) methods which are widely used in previous and current studies for Facial Weakness video classification. As the proposed camera-based analysis system requires no extra hardware, F-DIT-V could be implemented in a low-cost, portable, and easy to use format for generalizability to real world settings.
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abstract tp274 comparison of human and machine learning based Facial Weakness detection
Stroke, 2019Co-Authors: Mark Mcdonald, Yan Zhuang, Omar Uribe, Gustavo K Rohde, Iris Lin, Haydon Pitchford, William A Darlymple, Andrew M SoutherlandAbstract:Background: Prehospital stroke screening tools are essential to rapid diagnosis and treatment, but variable recognition of common stroke deficits by EMS providers limits the accuracy and reliabilit...
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pathological Facial Weakness detection using computational image analysis
International Symposium on Biomedical Imaging, 2018Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Daniel Arteaga, Andrew M Southerland, Iris Lin, William Dalrymple, Bradford B Worrall, Gustavo K RohdeAbstract:Stroke is the leading cause of disability in the U.S. Early recognition and treatment of stroke decreases the mortality and possibility of severe injury. One prominent stroke symptom is Facial Weakness. Existing methods such as telestroke systems, which require complex configuration including a tablet, a dedicated video conference system, 4G wireless connection, and a standby clinical support team for diagnosis, are far from widely practical use. In this pilot study, we present an automatic pathological Facial Weakness detection tool based on computational image analysis. The proposed system is able to extract the Facial landmarks and classify Facial Weakness using a learning method. In the experiment, each image in the dataset is scored independently by two senior neurology residents. Only images rated concordantly by both raters as likely normal or likely abnormal (including left Facial Weakness and right Facial Weakness) were included for analysis. Our method performed with overall accuracy of 94.5%, precision of 94.8%, sensitivity of 94.6%, and specificity of 96.8%. The experimental result indicates that our method is able to identify pathological Facial Weakness accurately on the static images.
Omar Uribe - One of the best experts on this subject based on the ideXlab platform.
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video based Facial Weakness analysis
IEEE Transactions on Biomedical Engineering, 2021Co-Authors: Yan Zhuang, Mark Mcdonald, Chad Aldridge, Mohamed Abul Hassan, Omar Uribe, Daniel Arteaga, Andrew M Southerland, Gustavo K RohdeAbstract:Objective: Facial Weakness is a common sign of neurological diseases such as Bell's palsy and stroke. However, recognizing Facial Weakness still remains as a challenge, because it requires experience and neurological training. Methods: We propose a framework for Facial Weakness detection, which models the temporal dynamics of both shape and appearance-based features of each target frame through a bi-directional long short-term memory network (Bi-LSTM). The system is evaluated on a in-the-wild video dataset that is verified by three board-certified neurologists. In addition, three emergency medical services (EMS) personnel and three upper level residents rated the dataset. We compare the evaluation of the proposed algorithm with other comparison methods as well as the human raters. Results: Experimental evaluation demonstrates that: (1) the proposed algorithm achieves the accuracy, sensitivity, and specificity of 94.3%, 91.4%, and 95.7%, which outperforms other comparison methods and achieves the equal performance to paramedics; (2) the framework can provide visualizable and interpretable results that increases model transparency and interpretability; (3) a prototype is implemented as a proof-of-concept showcase to show the feasibility of an inexpensive solution for Facial Weakness detection. Conclusion: The experiment results suggest that the proposed framework can identify Facial Weakness effectively. Significance: We provide a proof-of-concept study, showing that such technology could be used by non-neurologists to more readily identify Facial Weakness in the field, leading to increasing coverage and earlier treatment.
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Facial Weakness Analysis and Quantification of Static Images
IEEE Journal of Biomedical and Health Informatics, 2020Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Andrew M Southerland, Dhyey Parikh, Gustavo K RohdeAbstract:Facial Weakness is a symptom commonly associated to lack of Facial muscle control due to neurological injury. Several diseases are associated with Facial Weakness such as stroke and Bell's palsy. The use of digital imaging through mobile phones, tablets, personal computers and other devices could provide timely opportunity for detection, which if accurate enough can improve treatment by enabling faster patient triage and recovery progress monitoring. Most of the existing Facial Weakness detection approaches from static images are based on Facial landmarks from which geometric features can be calculated. Landmark-based methods, however, can suffer from inaccuracies in face landmarks localization. In this study, We also experimentally evaluate the performance of several feature extraction methods for measuring Facial Weakness, including the landmark-based features, as well as intensity-based features on a neurologist-certified dataset that comprises 186 images of normal, 125 images of left Facial Weakness, and 126 images of right Facial Weakness. We demonstrate that, for the application of Facial Weakness detection from single (static) images, approaches that incorporate the Histogram of Oriented Gradients (HoG) features tend to be more accurate.
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f dit v an automated video classification tool for Facial Weakness detection
IEEE-EMBS International Conference on Biomedical and Health Informatics, 2019Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Andrew M Southerland, Dhyey Parikh, Xuwang Yin, Gustavo K RohdeAbstract:Facial Weakness is a common presenting sign of several neurological diseases including stroke, traumatic brain injury (TBI), and Bell's palsy. Tools to improve the accuracy of Facial Weakness detection can prompt quicker evaluation into these diseases possibly resulting in earlier diagnoses. In this study, we propose an automated video classification detection tool, Facial Deficit Identification Tool for Videos (F-DIT-V), for Facial Weakness detection. This tool exploits Histogram of Oriented Gradients (HOG) features to perform more accurate Facial Weakness detection for a given video. Using experimental data we demonstrate that F-DIT-V achieves a classification accuracy of 92.9%, precision of 93.6%, recall of 92.8%, and specificity of 94.2%. F-DIT-V is able to achieve higher and more reliable performance compared to existing (e.g. LBP-TOP, RNN-based) methods which are widely used in previous and current studies for Facial Weakness video classification. As the proposed camera-based analysis system requires no extra hardware, F-DIT-V could be implemented in a low-cost, portable, and easy to use format for generalizability to real world settings.
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abstract tp274 comparison of human and machine learning based Facial Weakness detection
Stroke, 2019Co-Authors: Mark Mcdonald, Yan Zhuang, Omar Uribe, Gustavo K Rohde, Iris Lin, Haydon Pitchford, William A Darlymple, Andrew M SoutherlandAbstract:Background: Prehospital stroke screening tools are essential to rapid diagnosis and treatment, but variable recognition of common stroke deficits by EMS providers limits the accuracy and reliabilit...
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pathological Facial Weakness detection using computational image analysis
International Symposium on Biomedical Imaging, 2018Co-Authors: Yan Zhuang, Mark Mcdonald, Omar Uribe, Daniel Arteaga, Andrew M Southerland, Iris Lin, William Dalrymple, Bradford B Worrall, Gustavo K RohdeAbstract:Stroke is the leading cause of disability in the U.S. Early recognition and treatment of stroke decreases the mortality and possibility of severe injury. One prominent stroke symptom is Facial Weakness. Existing methods such as telestroke systems, which require complex configuration including a tablet, a dedicated video conference system, 4G wireless connection, and a standby clinical support team for diagnosis, are far from widely practical use. In this pilot study, we present an automatic pathological Facial Weakness detection tool based on computational image analysis. The proposed system is able to extract the Facial landmarks and classify Facial Weakness using a learning method. In the experiment, each image in the dataset is scored independently by two senior neurology residents. Only images rated concordantly by both raters as likely normal or likely abnormal (including left Facial Weakness and right Facial Weakness) were included for analysis. Our method performed with overall accuracy of 94.5%, precision of 94.8%, sensitivity of 94.6%, and specificity of 96.8%. The experimental result indicates that our method is able to identify pathological Facial Weakness accurately on the static images.
