Dynamic cheek surface modeling for enhanced hypomimia detection in Parkinson's disease

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00386783" target="_blank" >RIV/68407700:21230/25:00386783 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11110/25:10506817 RIV/00064165:_____/25:10506817

Výsledek na webu

<a href="https://doi.org/10.1016/j.compbiomed.2025.110896" target="_blank" >https://doi.org/10.1016/j.compbiomed.2025.110896</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2025.110896" target="_blank" >10.1016/j.compbiomed.2025.110896</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic cheek surface modeling for enhanced hypomimia detection in Parkinson's disease

Popis výsledku v původním jazyce

Hypomimia, or facial masking, is a prominent symptom in Parkinson's disease (PD), impacting up to 90% of patients and reducing their quality of life by impairing facial expressivity. Detecting hypomimia is challenging due to its subtle onset and the limited availability of robust diagnostic tools for automatic analysis. To address these gaps, we propose a novel approach based on cheek surface variability, which captures subtle, progressive changes in cheek movement across different temporal granularities within video frames. To evaluate the effectiveness of our cheek marker approach, we also implemented and compared machine learning techniques based on geometric and surface features, and a deep learning approach using a 3D Convolutional Neural Network (3D CNN) to capture the temporal dynamics of facial movements. Additionally, this methodology includes a robust pre-processing pipeline to mitigate head rotation and posture biases, ensuring a consistent frontal face orientation throughout the analysis. Video samples of spontaneous speech were collected from 112 de novo, drug naive PD patients and 90 healthy control (HC) subjects. Experimental results demonstrate that the proposed cheek surface variability approach outperformed both the machine learning and deep learning models, achieving an unweighted averaged recall of 80.6% and emphasizing its effectiveness in distinguishing PD patients from HC subjects. This work highlights the importance of integrating temporal dynamics into hypomimia modeling and presents cheek variability as a valuable biomarker that enhances classification accuracy. It offers promising implications for more precise monitoring and diagnosis of PD.

Název v anglickém jazyce

Dynamic cheek surface modeling for enhanced hypomimia detection in Parkinson's disease

Popis výsledku anglicky

Hypomimia, or facial masking, is a prominent symptom in Parkinson's disease (PD), impacting up to 90% of patients and reducing their quality of life by impairing facial expressivity. Detecting hypomimia is challenging due to its subtle onset and the limited availability of robust diagnostic tools for automatic analysis. To address these gaps, we propose a novel approach based on cheek surface variability, which captures subtle, progressive changes in cheek movement across different temporal granularities within video frames. To evaluate the effectiveness of our cheek marker approach, we also implemented and compared machine learning techniques based on geometric and surface features, and a deep learning approach using a 3D Convolutional Neural Network (3D CNN) to capture the temporal dynamics of facial movements. Additionally, this methodology includes a robust pre-processing pipeline to mitigate head rotation and posture biases, ensuring a consistent frontal face orientation throughout the analysis. Video samples of spontaneous speech were collected from 112 de novo, drug naive PD patients and 90 healthy control (HC) subjects. Experimental results demonstrate that the proposed cheek surface variability approach outperformed both the machine learning and deep learning models, achieving an unweighted averaged recall of 80.6% and emphasizing its effectiveness in distinguishing PD patients from HC subjects. This work highlights the importance of integrating temporal dynamics into hypomimia modeling and presents cheek variability as a valuable biomarker that enhances classification accuracy. It offers promising implications for more precise monitoring and diagnosis of PD.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Computers in Biology and Medicine

ISSN

0010-4825

e-ISSN

1879-0534

Svazek periodika

197

Číslo periodika v rámci svazku

Octover

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105015965112