Outcome Prediction of Bell’s Palsy by Kinect II

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F19%3A43920064" target="_blank" >RIV/60461373:22340/19:43920064 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/19:00339291

Výsledek na webu

<a href="https://biomedres.us/fulltexts/BJSTR.MS.ID.002616.php" target="_blank" >https://biomedres.us/fulltexts/BJSTR.MS.ID.002616.php</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.26717/BJSTR.2019.14.002616" target="_blank" >10.26717/BJSTR.2019.14.002616</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Outcome Prediction of Bell’s Palsy by Kinect II

Popis výsledku v původním jazyce

Purpose: Bell’s palsy is a facial paralysis resulting from the 7th cranial nerve lesion. The House-Brackmann facial nerve grading system is widely used to characterize the severity of an attack. Like other subjective clinical scales it has insufficient inter-rater agreement. Prediction value of House-Brackmann grading scale and distance measures of Kinect 2 images for outcome of unilateral Bell’s palsy was compared. Methods: Five mimic muscles were tested by voluntary contraction. Data was recorded by high definition face tracking mode of Kinect 2. Corresponding virtual markers on both sides of the face was identified during neutral facial expression by affine transformation. The proportions of trajectories of markers on the affected and unaffected side of the face indicate the degree of muscle disability. For prediction of patient’s outcome the House-Brackmann grading scale, Hausdorf and Euclidean distance in the first and second examination was used. The third examination after 6 months served to determine the outcome. Data Analysis: Binary support vector machine classifier with leave-one-out cross-validation was used for prediction of outcome. Results: House-Brackmann grading scale outperformed distance measures. Hausdorf distance had comparable prediction value while Euclidean distance had the lowest prediction value. Conclusion: Lower predictive value of distance measures may be due to low accuracy of the Kinect 2 depth sensor. With the rapid development of 3D scanners, greater accuracy of scanning and therefore better outcome prediction of patient’s with Bell’s palsy can be expected.

Název v anglickém jazyce

Outcome Prediction of Bell’s Palsy by Kinect II

Popis výsledku anglicky

Purpose: Bell’s palsy is a facial paralysis resulting from the 7th cranial nerve lesion. The House-Brackmann facial nerve grading system is widely used to characterize the severity of an attack. Like other subjective clinical scales it has insufficient inter-rater agreement. Prediction value of House-Brackmann grading scale and distance measures of Kinect 2 images for outcome of unilateral Bell’s palsy was compared. Methods: Five mimic muscles were tested by voluntary contraction. Data was recorded by high definition face tracking mode of Kinect 2. Corresponding virtual markers on both sides of the face was identified during neutral facial expression by affine transformation. The proportions of trajectories of markers on the affected and unaffected side of the face indicate the degree of muscle disability. For prediction of patient’s outcome the House-Brackmann grading scale, Hausdorf and Euclidean distance in the first and second examination was used. The third examination after 6 months served to determine the outcome. Data Analysis: Binary support vector machine classifier with leave-one-out cross-validation was used for prediction of outcome. Results: House-Brackmann grading scale outperformed distance measures. Hausdorf distance had comparable prediction value while Euclidean distance had the lowest prediction value. Conclusion: Lower predictive value of distance measures may be due to low accuracy of the Kinect 2 depth sensor. With the rapid development of 3D scanners, greater accuracy of scanning and therefore better outcome prediction of patient’s with Bell’s palsy can be expected.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biomedical Journal of Scientific &amp; Technical Research

ISSN

2574-1241

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

4

Strana od-do

"1 "- 4

Kód UT WoS článku

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EID výsledku v databázi Scopus

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