<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this study, we propose to diagnose facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial palsy domain using facial features and grade the degree of nerve damage based on the House-Brackmann score. Traditional diagnostic approaches involve a medical doctor recording a thorough history of a patient and determining the onset of paralysis, rate of progression and so on. The most important step is to assess the degree of voluntary movement of the facial nerves and document the grade of facial paralysis using House- Brackmann score. The significance of the work is the attempt to understand the diagnosis and grading processes using semi-supervised learning with the aim of automating the process. The value of the research is in identifying and documenting the limited literature seen in this area. The use of automated diagnosis and grading greatly reduces the duration of medical examination and increases the consistency, because many palsy images are stored to provide benchmark references for comparative purposes. The proposed automated diagnosis and grading are computationally efficient. This automated process makes it ideal for remote diagnosis and examination of facial palsy. The profiling of a large number of facial images are captured using mobile phones and digital cameras.</p></abstract>

In this study, we propose to diagnose facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial palsy domain using facial features and grade the degree of nerve damage based on the House-Brackmann score. Traditional diagnostic approaches involve a medical doctor recording a thorough history of a patient and determining the onset of paralysis, rate of progression and so on. The most important step is to assess the degree of voluntary movement of the facial nerves and document the grade of facial paralysis using House- Brackmann score. The significance of the work is the attempt to understand the diagnosis and grading processes using semi-supervised learning with the aim of automating the process. The value of the research is in identifying and documenting the limited literature seen in this area. The use of automated diagnosis and grading greatly reduces the duration of medical examination and increases the consistency, because many palsy images are stored to provide benchmark references for comparative purposes. The proposed automated diagnosis and grading are computationally efficient. This automated process makes it ideal for remote diagnosis and examination of facial palsy. The profiling of a large number of facial images are captured using mobile phones and digital cameras.

Profiling Bell’s Palsy based on House-Brackmann Score

School of Business (IT), James Cook University, Singapore campus, 600 Upper Thomson Road, Singapore

Department of Psychiatry and Behavioral Sciences, UC Davis

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Profiling Bell\u2019s Palsy based on House-Brackmann Score"}

In this study, we propose to diagnose facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to...

Profiling Bell’s Palsy based on House-Brackmann Score

Publicado en línea: 30 dic 2014

Páginas: 41 - 50

DOI: https://doi.org/10.2478/jaiscr-2014-0004

© 2015

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.