Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online

Abstract A prevalent symptom of Parkinson’s disease (PD) is hypomimia — reduced facial expressions. In this paper, we present a method for diagnosing PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 with...

Description complète

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Mohammad Rafayet Ali, Taylor Myers, Ellen Wagner, Harshil Ratnu, E. Ray Dorsey, Ehsan Hoque
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2021
Sujets:	Computer applications to medicine. Medical informatics R858-859.7
Accès en ligne:	https://doaj.org/article/614daa2b2e2d48b09ec72bffcfe173ac
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

id	oai:doaj.org-article:614daa2b2e2d48b09ec72bffcfe173ac
record_format	dspace
spelling	oai:doaj.org-article:614daa2b2e2d48b09ec72bffcfe173ac2021-12-02T15:29:01ZFacial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online10.1038/s41746-021-00502-82398-6352https://doaj.org/article/614daa2b2e2d48b09ec72bffcfe173ac2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00502-8https://doaj.org/toc/2398-6352Abstract A prevalent symptom of Parkinson’s disease (PD) is hypomimia — reduced facial expressions. In this paper, we present a method for diagnosing PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, with a mean age 63.9 y/o, sd. 7.8) collected online through a web-based tool ( www.parktest.net ). In these videos, participants were asked to make three facial expressions (a smiling, disgusted, and surprised face) followed by a neutral face. Using techniques from computer vision and machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to methodologies that utilize motor symptoms. Logistic regression analysis revealed that participants with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals. An automated classifier using Support Vector Machine was trained on the variances and achieved 95.6% accuracy. Using facial expressions as a future digital biomarker for PD could be potentially transformative for patients in need of remote diagnoses due to physical separation (e.g., due to COVID) or immobility.Mohammad Rafayet AliTaylor MyersEllen WagnerHarshil RatnuE. Ray DorseyEhsan HoqueNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Mohammad Rafayet Ali Taylor Myers Ellen Wagner Harshil Ratnu E. Ray Dorsey Ehsan Hoque Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online
description	Abstract A prevalent symptom of Parkinson’s disease (PD) is hypomimia — reduced facial expressions. In this paper, we present a method for diagnosing PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, with a mean age 63.9 y/o, sd. 7.8) collected online through a web-based tool ( www.parktest.net ). In these videos, participants were asked to make three facial expressions (a smiling, disgusted, and surprised face) followed by a neutral face. Using techniques from computer vision and machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to methodologies that utilize motor symptoms. Logistic regression analysis revealed that participants with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals. An automated classifier using Support Vector Machine was trained on the variances and achieved 95.6% accuracy. Using facial expressions as a future digital biomarker for PD could be potentially transformative for patients in need of remote diagnoses due to physical separation (e.g., due to COVID) or immobility.
format	article
author	Mohammad Rafayet Ali Taylor Myers Ellen Wagner Harshil Ratnu E. Ray Dorsey Ehsan Hoque
author_facet	Mohammad Rafayet Ali Taylor Myers Ellen Wagner Harshil Ratnu E. Ray Dorsey Ehsan Hoque
author_sort	Mohammad Rafayet Ali
title	Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online
title_short	Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online
title_full	Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online
title_fullStr	Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online
title_full_unstemmed	Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online
title_sort	facial expressions can detect parkinson’s disease: preliminary evidence from videos collected online
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/614daa2b2e2d48b09ec72bffcfe173ac
work_keys_str_mv	AT mohammadrafayetali facialexpressionscandetectparkinsonsdiseasepreliminaryevidencefromvideoscollectedonline AT taylormyers facialexpressionscandetectparkinsonsdiseasepreliminaryevidencefromvideoscollectedonline AT ellenwagner facialexpressionscandetectparkinsonsdiseasepreliminaryevidencefromvideoscollectedonline AT harshilratnu facialexpressionscandetectparkinsonsdiseasepreliminaryevidencefromvideoscollectedonline AT eraydorsey facialexpressionscandetectparkinsonsdiseasepreliminaryevidencefromvideoscollectedonline AT ehsanhoque facialexpressionscandetectparkinsonsdiseasepreliminaryevidencefromvideoscollectedonline
_version_	1718387144185085952

Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online

Documents similaires