Video Assessment to Detect Amyotrophic Lateral Sclerosis.

Q1 Computer Science Digital Biomarkers Pub Date : 2024-08-29 eCollection Date: 2024-01-01 DOI:10.1159/000540547

Guilherme Camargo Oliveira, Quoc Cuong Ngo, Leandro Aparecido Passos, Leonardo Silva Oliveira, Stella Stylianou, João Paulo Papa, Dinesh Kumar

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Abstract

Introduction: Weakened facial movements are early-stage symptoms of amyotrophic lateral sclerosis (ALS). ALS is generally detected based on changes in facial expressions, but large differences between individuals can lead to subjectivity in the diagnosis. We have proposed a computerized analysis of facial expression videos to detect ALS.

Methods: This study investigated the action units obtained from facial expression videos to differentiate between ALS patients and healthy individuals, identifying the specific action units and facial expressions that give the best results. We utilized the Toronto NeuroFace Dataset, which includes nine facial expression tasks for healthy individuals and ALS patients.

Results: The best classification accuracy was 0.91 obtained for the pretending to smile with tight lips expression.

Conclusion: This pilot study shows the potential of using computerized facial expression analysis based on action units to identify facial weakness symptoms in ALS.

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通过视频评估检测肌萎缩性脊髓侧索硬化症

简介面部动作减弱是肌萎缩性脊髓侧索硬化症（ALS）的早期症状。一般根据面部表情的变化来检测 ALS，但个体之间的巨大差异会导致诊断的主观性。我们提出了一种通过计算机分析面部表情视频来检测 ALS 的方法：本研究调查了从面部表情视频中获得的动作单元，以区分 ALS 患者和健康人，并确定了效果最佳的特定动作单元和面部表情。我们使用了多伦多神经脸部数据集，其中包括针对健康人和 ALS 患者的九项面部表情任务：结果："紧闭嘴唇假装微笑 "表情的最佳分类准确率为 0.91：这项试验研究表明，基于动作单元的计算机化面部表情分析具有识别 ALS 患者面部无力症状的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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