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Background <p>Neurological disorders, ranging from common conditions like Alzheimer’s disease that is a progressive neurodegenerative disorder and remains the most common cause of dementia worldwide to rare disorders such as Angelman syndrome, impose a significant global health burden. Altered facial expressions are a common symptom across these disorders, potentially serving as a diagnostic indicator. Deep learning algorithms, especially convolutional neural networks (CNNs), have shown promise in detecting these facial expression changes, aiding in diagnosing and monitoring neurological conditions.</p> Objectives <p>This systematic review and meta-analysis aimed to evaluate the performance of deep learning algorithms in detecting facial expression changes for diagnosing neurological disorders.</p> Methods <p>Following PRISMA2020 guidelines, we systematically searched PubMed, Scopus, and Web of Science for studies published up to August 2024. Data from 28 studies were extracted, and the quality was assessed using the JBI checklist. A meta-analysis was performed to calculate pooled accuracy estimates. Subgroup analyses were conducted based on neurological disorders, and heterogeneity was evaluated using the <i>I</i><sup>2</sup> statistic.</p> Results <p>The meta-analysis included 24 studies from 2019 to 2024, with neurological conditions such as dementia, Bell’s palsy, ALS, and Parkinson’s disease assessed. The overall pooled accuracy was 89.25% (95% CI 88.75–89.73%). High accuracy was found for dementia (99%) and Bell’s palsy (93.7%), while conditions such as ALS and stroke had lower accuracy (73.2%).</p> Conclusions <p>Deep learning models, particularly CNNs, show strong potential in detecting facial expression changes for neurological disorders. However, further work is needed to standardize data sets and improve model robustness for motor-related conditions.</p>

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Facial expression deep learning algorithms in the detection of neurological disorders: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月22日

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  • Shania Yoonesi,
  • Ramila Abedi Azar,
  • Melika Arab Bafrani,
  • Shayan Yaghmayee,
  • Haniye Shahavand,
  • Majid Mirmazloumi,
  • Narges Moazeni Limoudehi,
  • Mohammadreza Rahmani,
  • Saina Hasany,
  • Fatemeh Zahra Idjadi,
  • Mohammad Amin Aalipour,
  • Hossein Gharedaghi,
  • Sadaf Salehi,
  • Mahsa Asadi Anar,
  • Mohammad Saeed Soleimani

摘要

Background

Neurological disorders, ranging from common conditions like Alzheimer’s disease that is a progressive neurodegenerative disorder and remains the most common cause of dementia worldwide to rare disorders such as Angelman syndrome, impose a significant global health burden. Altered facial expressions are a common symptom across these disorders, potentially serving as a diagnostic indicator. Deep learning algorithms, especially convolutional neural networks (CNNs), have shown promise in detecting these facial expression changes, aiding in diagnosing and monitoring neurological conditions.

Objectives

This systematic review and meta-analysis aimed to evaluate the performance of deep learning algorithms in detecting facial expression changes for diagnosing neurological disorders.

Methods

Following PRISMA2020 guidelines, we systematically searched PubMed, Scopus, and Web of Science for studies published up to August 2024. Data from 28 studies were extracted, and the quality was assessed using the JBI checklist. A meta-analysis was performed to calculate pooled accuracy estimates. Subgroup analyses were conducted based on neurological disorders, and heterogeneity was evaluated using the I2 statistic.

Results

The meta-analysis included 24 studies from 2019 to 2024, with neurological conditions such as dementia, Bell’s palsy, ALS, and Parkinson’s disease assessed. The overall pooled accuracy was 89.25% (95% CI 88.75–89.73%). High accuracy was found for dementia (99%) and Bell’s palsy (93.7%), while conditions such as ALS and stroke had lower accuracy (73.2%).

Conclusions

Deep learning models, particularly CNNs, show strong potential in detecting facial expression changes for neurological disorders. However, further work is needed to standardize data sets and improve model robustness for motor-related conditions.