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Recognition and classification of facial expression using artificial intelligence as a key of early detection in neurological disorders

  • Nooshin Goudarzi , Zahra Taheri , Amir Mohammad Nezhad Salari , Kimia Kazemzadeh und Abbas Tafakhori EMAIL logo
Veröffentlicht/Copyright: 21. Januar 2025

Abstract

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.


Corresponding author: Abbas Tafakhori, Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran; Iranian Center of Neurological Research, Neuroscience Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, 1419733141, Iran; and Department of Neurology, School of Medicine, Tehran University of Medical Sciences, Tehran, 1416634793, Iran, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  4. Author contributions: N.G., Z.T., and AM.NS. wrote the original draft of the manuscript. K.K. supervised, reviewed, and edited the manuscript. A.T. validated and supervised the manuscript. All authors reviewed the final draft.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2024-09-14
Accepted: 2024-12-22
Published Online: 2025-01-21
Published in Print: 2025-07-28

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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