Video-Based Facial Movement Analysis in the Assessment of Bulbar Amyotrophic Lateral Sclerosis: Clinical Validation.
Purpose: Facial movement analysis during facial gestures and speech provides clinically useful information for assessing bulbar amyotrophic lateral sclerosis (ALS). However, current kinematic methods have limited clinical application due to the equipment costs. Recent advancements in consumer-grade...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 65; no. 12; pp. 4667 - 4679 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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American Speech-Language-Hearing Association
Dec2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=160768014&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 160768014 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Dec2022 vid: 65 iid: 12 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 160768014 10.1044/2022_JSLHR-22-00072 ppf: 4667 ppct: 12 formats: fmt: @attributes: type: P size: 1MB tig: atl: Video-Based Facial Movement Analysis in the Assessment of Bulbar Amyotrophic Lateral Sclerosis: Clinical Validation. aug: au: Guarin, Diego L. Taati, Babak Abrahao, Agessandro Zinman, Lorne Yunusova, Yana affil: Department of Applied Physiology and Kinesiology, University of Florida, Gainesville. KITE–Toronto Rehabilitation Institute, University Health Network, Ontario, Canada. Department of Computer Science, University of Toronto, Ontario, Canada. Institute of Biomedical Engineering, University of Toronto, Ontario, Canada. Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. Division of Neurology, Department of Medicine, University of Toronto, Ontario, Canada. L.C. Campbell Cognitive Neurology Research Unit, Cognitive Neurology, Sunnybrook Research Institute, University of Toronto, Ontario, Canada. Department of Speech-Language Pathology and Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada. su: Human research subjects Self-evaluation Facial expression Informed consent (Medical law) Deep learning Relative medical risk Progressive bulbar palsy Machine learning T-test (Statistics) Amyotrophic lateral sclerosis Descriptive statistics Research funding Statistical sampling Kinematics Video recording Algorithms Probability theory sug: subj: Human research subjects Self-evaluation Facial expression Informed consent (Medical law) Marketing Research and Public Opinion Polling Deep learning Relative medical risk Progressive bulbar palsy Machine learning T-test (Statistics) Amyotrophic lateral sclerosis Descriptive statistics Research funding Statistical sampling Kinematics Video recording Algorithms Probability theory ab: Purpose: Facial movement analysis during facial gestures and speech provides clinically useful information for assessing bulbar amyotrophic lateral sclerosis (ALS). However, current kinematic methods have limited clinical application due to the equipment costs. Recent advancements in consumer-grade hardware and machine/deep learning made it possible to estimate facial movements from videos. This study aimed to establish the clinical validity of a video-based facial analysis for disease staging classification and estimation of clinical scores. Method: Fifteen individuals with ALS and 11 controls participated in this study. Participants with ALS were stratified into early and late bulbar ALS groups based on their speaking rate. Participants were recorded with a three-dimensional (3D) camera (color + depth) while repeating a simple sentence 10 times. The lips and jaw movements were estimated, and features related to sentence duration and facial movements were used to train a machine learning model for multiclass classification and to predict the Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised (ALSFRS-R) bulbar subscore and speaking rate. Results: The classification model successfully separated healthy controls, the early ALS group, and the late ALS group with an overall accuracy of 96.1%. Video-based features demonstrated a high ability to estimate the speaking rate (adjusted R² = .82) and a moderate ability to predict the ALSFRS-R bulbar subscore (adjusted R² = .55). Conclusions: The proposed approach based on a 3D camera and machine learning algorithms represents an easy-to-use and inexpensive system that can be included as part of a clinical assessment of bulbar ALS to integrate facial movement analysis with other clinical data seamlessly. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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