Profiling bell's palsy based on House-Brackmann score

Song, Insu; Nguwi, Yok-Yen; Vong, John; Diederich, Joahchim; Yellowlees, Peter Mackinlay

doi:10.1109/cicare.2013.6583060

Cited by 14 publications

(12 citation statements)

References 13 publications

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“…The House-Brackmann system (HBS) [8] and the Sunnybrook facial grading system [9] are the two main scales used for the evaluation of FNP. HBS is widely used for scoring facial paralysis due to its reliability, brevity, accuracy and ease of understanding [10], [11]. It grades facial palsy in six scores from normal (Grade 1) to total paralysis (Grade 6) according to functional performance of facial muscle, which is shown in Table Ⅰ.…”

Section: Introductionmentioning

confidence: 99%

Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

Liu

Xia

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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Section: Introductionmentioning

confidence: 99%

Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

Liu

Xia

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…As an alternative to standard video imaging, 3D measurements generated from space-coding method 98 as well as Kinect V2 99, 100 have been suggested to detect facial asymmetries by retrieving 3D coordinates of facial landmarks. Different studies tried to provide an objective solution to quantify the severity of the condition [84], [86], [89], [99], using the House-Brackmann facial nerve grading as a reference scale. However, this grading system has been criticized as it does not score accurately each facial function [101].…”

Section: Muscular Basismentioning

confidence: 99%

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces

Thevenot

López

Hadid

2018

IEEE J. Biomed. Health Inform.

137

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Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives, such as the need for interdisciplinary collaboration and collecting publicly available databases, are highlighted.

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“…Since the difference between grade II-III and IV-V is small, it is possible to use a reduced House-Brackmann index with only four degrees of paresis (Gebhard et al, 2001). With the use of local binary patterns or the Hamming distance between the left and the right side of the face and a classification method the entire HB index can be predicted (He et al, 2009;Song et al, 2013).…”

Section: Grade Dysfunctionmentioning

confidence: 99%

“…A prediction method for the House-Brackmann score based only on a few single images was developed (Song et al, 2013). After detecting the edges of a patients image they calculated the Hamming distance for facial symmetry.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Facial Paresis Index Prediction by Exploiting Active Appearance Models for Compact Discriminative Features

Modersohn

Denzler

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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In the field of otorhinolaryngology, the dysfunction of the facial nerve is a common disease which results in a paresis of usually one half of the patients face. The grade of paralysis is measured by physicians with rating scales, e.g. the Stennert Index or the House-Brackmann scale. In this work, we propose a method to analyse and predict the severity of facial paresis on the basis of single images. We combine feature extraction methods based on a generative approach (Active Appearance Models) with a fast non-linear classifier (Random Decision Forests) in order to predict the patients grade of facial paresis. In our proposed framework, we make use of highly discriminative features based on the fitting parameters of the Active Appearance Model, Action Units and Landmark distances. We show in our experiments that it is possible to correctly predict the grade of facial paresis in many cases, although the visual appearance is strongly varying. The presented method creates new opportunities to objectively document the patients progress in therapy.

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Profiling bell's palsy based on House-Brackmann score

Cited by 14 publications

References 13 publications

Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces

Facial Paresis Index Prediction by Exploiting Active Appearance Models for Compact Discriminative Features

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