儿童癫痫检测中的人工智能:平衡有效性与福利的伦理考虑。

IF 2.1 Q2 MEDICINE, GENERAL & INTERNAL Health Science Reports Pub Date : 2025-01-22 DOI:10.1002/hsr2.70372
Marina Ramzy Mourid, Hamza Irfan, Malik Olatunde Oduoye
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引用次数: 0

摘要

背景和目的:癫痫是一种主要的神经学挑战,特别是对儿科人群。它深刻地影响着患病儿童的发展进程和生活质量。随着人工智能(AI)的出现,人们越来越有兴趣利用其能力来改善儿科癫痫的诊断和管理。本综述旨在评估人工智能在儿童癫痫检测中的有效性,同时考虑其实施的伦理影响。方法:对包括PubMed、EMBASE、谷歌Scholar、Scopus和Medline在内的多个数据库进行了全面的系统评价。搜索词包括“儿童癫痫”、“人工智能”、“机器学习”、“伦理考虑”和“数据安全”。对过去十年的出版物进行了方法学严谨性审查,重点是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的潜力,通常与临床准确性相匹配。例如,人工智能驱动的决策支持在诊断方面达到了93.4%的准确率,与专家评估非常接近。特异性方法,如基于脑电图的人工智能检测间歇放电,具有较高的特异性(93.33%-96.67%)和敏感性(76.67%-93.33%),而使用rs-fMRI和DTI的神经影像学方法识别微结构异常的准确率可达97.5%。CNN-LSTM等深度学习模型也通过捕捉细微的动作和表情线索,增强了对视频的癫痫检测。基于非脑电图传感器的方法有效地识别夜间癫痫发作,为儿科护理提供了有希望的支持。然而,关于隐私、数据安全和模型偏差的道德考虑对于负责任的人工智能集成仍然至关重要。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、公平性和数据安全性的伦理问题。为了有效应对这些伦理挑战,确保负责任的人工智能整合并优化儿童癫痫治疗中的患者结果,利益攸关方之间的合作努力势在必行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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.

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来源期刊
Health Science Reports
Health Science Reports Medicine-Medicine (all)
CiteScore
1.80
自引率
0.00%
发文量
458
审稿时长
20 weeks
期刊最新文献
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