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人工智能在小儿癫痫检测中的应用:在有效性与福利伦理考量之间寻求平衡

Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare.

作者信息

Mourid Marina Ramzy, Irfan Hamza, Oduoye Malik Olatunde

机构信息

Faculty of Medicine Alexandria University Alexandria Egypt.

Department of Medicine Shaikh Khalifa Bin Zayed Al Nahyan Medical and Dental College Lahore Pakistan.

出版信息

Health Sci Rep. 2025 Jan 22;8(1):e70372. doi: 10.1002/hsr2.70372. eCollection 2025 Jan.

Abstract

BACKGROUND AND AIM

Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress and quality of life in affected children. With the advent of artificial intelligence (AI), there's a growing interest in leveraging its capabilities to improve the diagnosis and management of pediatric epilepsy. This review aims to assess the effectiveness of AI in pediatric epilepsy detection while considering the ethical implications surrounding its implementation.

METHODOLOGY

A comprehensive systematic review was conducted across multiple databases including PubMed, EMBASE, Google Scholar, Scopus, and Medline. Search terms encompassed "pediatric epilepsy," "artificial intelligence," "machine learning," "ethical considerations," and "data security." Publications from the past decade were scrutinized for methodological rigor, with a focus on studies evaluating AI's efficacy in pediatric epilepsy detection and management.

RESULTS

AI systems have demonstrated strong potential in diagnosing and monitoring pediatric epilepsy, often matching clinical accuracy. For example, AI-driven decision support achieved 93.4% accuracy in diagnosis, closely aligning with expert assessments. Specific methods, like EEG-based AI for detecting interictal discharges, showed high specificity (93.33%-96.67%) and sensitivity (76.67%-93.33%), while neuroimaging approaches using rs-fMRI and DTI reached up to 97.5% accuracy in identifying microstructural abnormalities. Deep learning models, such as CNN-LSTM, have also enhanced seizure detection from video by capturing subtle movement and expression cues. Non-EEG sensor-based methods effectively identified nocturnal seizures, offering promising support for pediatric care. However, ethical considerations around privacy, data security, and model bias remain crucial for responsible AI integration.

CONCLUSION

While AI holds immense potential to enhance pediatric epilepsy management, ethical considerations surrounding transparency, fairness, and data security must be rigorously addressed. Collaborative efforts among stakeholders are imperative to navigate these ethical challenges effectively, ensuring responsible AI integration and optimizing patient outcomes in pediatric epilepsy care.

摘要

背景与目的

癫痫是一项重大的神经学挑战,对儿童群体而言尤甚。它对受影响儿童的发育进程和生活质量均产生深远影响。随着人工智能(AI)的出现,利用其能力改善儿童癫痫的诊断和管理的兴趣与日俱增。本综述旨在评估人工智能在儿童癫痫检测中的有效性,同时考虑其实施过程中的伦理问题。

方法

在包括PubMed、EMBASE、谷歌学术、Scopus和Medline在内的多个数据库中进行了全面的系统综述。检索词包括“儿童癫痫”“人工智能”“机器学习”“伦理考量”和“数据安全”。对过去十年的出版物进行了方法严谨性审查,重点关注评估人工智能在儿童癫痫检测和管理中的功效的研究。

结果

人工智能系统在诊断和监测儿童癫痫方面显示出强大潜力,通常与临床准确性相当。例如,人工智能驱动的决策支持在诊断中达到了93.4%的准确率,与专家评估结果相近。特定方法,如基于脑电图的人工智能检测发作间期放电,显示出高特异性(93.33%-96.67%)和敏感性(76.67%-93.33%),而使用静息态功能磁共振成像(rs-fMRI)和扩散张量成像(DTI)的神经影像学方法在识别微观结构异常方面的准确率高达97.5%。深度学习模型,如卷积神经网络-长短期记忆网络(CNN-LSTM),也通过捕捉细微的运动和表情线索增强了从视频中检测癫痫发作的能力。基于非脑电图传感器的方法有效地识别了夜间癫痫发作,为儿童护理提供了有前景的支持。然而,围绕隐私、数据安全和模型偏差的伦理考量对于负责任地整合人工智能仍然至关重要。

结论

虽然人工智能在加强儿童癫痫管理方面具有巨大潜力,但必须严格解决围绕透明度、公平性和数据安全的伦理问题。利益相关者之间的合作努力对于有效应对这些伦理挑战至关重要,以确保在儿童癫痫护理中负责任地整合人工智能并优化患者治疗效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0a3/11751886/f6ca531104dc/HSR2-8-e70372-g001.jpg

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