面部表情深度学习算法在神经系统疾病检测中的应用:系统综述和荟萃分析。

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
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
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引用次数: 0

摘要

背景:神经系统疾病,从阿尔茨海默病(一种进行性神经退行性疾病,仍是世界范围内痴呆症的最常见原因)等常见疾病到Angelman综合征等罕见疾病,都给全球健康造成了重大负担。面部表情改变是这些疾病的常见症状,可能作为诊断指标。深度学习算法,尤其是卷积神经网络(cnn),在检测这些面部表情变化、帮助诊断和监测神经系统疾病方面显示出了希望。目的:本系统综述和荟萃分析旨在评估深度学习算法在检测面部表情变化诊断神经系统疾病方面的性能。方法:按照PRISMA2020指南,我们系统地检索PubMed、Scopus和Web of Science,检索截至2024年8月发表的研究。从28项研究中提取数据,并使用JBI检查表评估质量。进行荟萃分析以计算汇总的准确度估计值。根据神经系统疾病进行亚组分析,并使用I2统计量评估异质性。结果:荟萃分析包括2019年至2024年的24项研究,评估了痴呆症、贝尔氏麻痹症、ALS和帕金森病等神经系统疾病。总体合并准确率为89.25% (95% CI 88.75-89.73%)。痴呆(99%)和贝尔麻痹(93.7%)的准确率较高,而ALS和中风等疾病的准确率较低(73.2%)。结论:深度学习模型,特别是cnn,在检测神经系统疾病的面部表情变化方面显示出强大的潜力。然而,需要进一步的工作来标准化数据集和提高运动相关条件的模型鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Facial expression deep learning algorithms in the detection of neurological disorders: a systematic review and meta-analysis.

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.

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来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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