使用人工智能识别和分类面部表情是早期发现神经系统疾病的关键。

IF 3.4 3区 医学 Q2 NEUROSCIENCES Reviews in the Neurosciences Pub Date : 2025-01-21 DOI:10.1515/revneuro-2024-0125
Nooshin Goudarzi, Zahra Taheri, Amir Mohammad Nezhad Salari, Kimia Kazemzadeh, Abbas Tafakhori
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引用次数: 0

摘要

利用人工智能(AI)对面部表情进行识别和分类,为神经退行性疾病的早期检测和监测提供了一条有前途的途径。这篇叙述性综述批判性地审视了人工智能驱动的面部表情分析在神经退行性疾病(如阿尔茨海默病和帕金森病)背景下的现状。我们讨论了人工智能技术的潜力,包括深度学习和计算机视觉,以准确地解释和分类与这些病理条件相关的面部表情的细微变化。此外,我们还探讨了面部表情识别作为一种无创、成本效益高的工具,在神经退行性疾病的筛查、疾病进展跟踪和个性化干预方面的作用。该综述还讨论了将基于人工智能的面部表情分析整合到临床实践中的挑战、伦理考虑和未来前景,以早期干预和改善有神经退行性疾病风险或受其影响的个体的生活质量。
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Recognition and classification of facial expression using artificial intelligence as a key of early detection in neurological disorders.

The recognition and classification of facial expressions using artificial intelligence (AI) presents a promising avenue for early detection and monitoring of neurodegenerative disorders. This narrative review critically examines the current state of AI-driven facial expression analysis in the context of neurodegenerative diseases, such as Alzheimer's and Parkinson's. We discuss the potential of AI techniques, including deep learning and computer vision, to accurately interpret and categorize subtle changes in facial expressions associated with these pathological conditions. Furthermore, we explore the role of facial expression recognition as a noninvasive, cost-effective tool for screening, disease progression tracking, and personalized intervention in neurodegenerative disorders. The review also addresses the challenges, ethical considerations, and future prospects of integrating AI-based facial expression analysis into clinical practice for early intervention and improved quality of life for individuals at risk of or affected by neurodegenerative diseases.

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来源期刊
Reviews in the Neurosciences
Reviews in the Neurosciences 医学-神经科学
CiteScore
9.40
自引率
2.40%
发文量
54
审稿时长
6-12 weeks
期刊介绍: Reviews in the Neurosciences provides a forum for reviews, critical evaluations and theoretical treatment of selective topics in the neurosciences. The journal is meant to provide an authoritative reference work for those interested in the structure and functions of the nervous system at all levels of analysis, including the genetic, molecular, cellular, behavioral, cognitive and clinical neurosciences. Contributions should contain a critical appraisal of specific areas and not simply a compilation of published articles.
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