儿童神经发育障碍诊断中的人工智能:一个范围审查
María Alejandra Nieto Ramírez1, Mateo Mariño Rodríguez1, María José Castro Salas1
1School of Medicine, Universidad de La Sabana, Chía, Colombia.
World journal of pediatrics : WJP
|January 27, 2026
概括
人工智能 (AI) 增强了儿童神经发育障碍的诊断. 人工智能工具在神经成像和分类方面显示出高精度,改善了早期检测和临床决策,尽管道德考虑和标准化是关键.
科学领域:
- 儿童神经发育障碍 儿童神经发育障碍
- 医疗保健中的人工智能
- 诊断的准确性 诊断的准确性
背景情况:
- 神经发育障碍显著影响儿童的生活质量,需要准确的诊断工具.
- 人工智能 (AI) 在诊断和监测这些疾病方面表现出有效性.
- 本综述侧重于人工智能在改善儿科神经发育障碍的诊断准确度方面的作用.
研究的目的:
- 总结当前关于人工智能技术用于诊断儿科神经发育障碍的证据.
- 审查深度学习,监督机器学习,决策支持系统和生物信号分析的应用.
- 评估AI对诊断准确性和临床决策的影响.
主要方法:
- 在主要数据库 (PubMed, LILACS, MEDLINE, Google Scholar) 和心理学期刊 (2000-2025) 中进行系统搜索.
- 系统性审查和元分析中优先报告项的应用,扩展范围审查 (PRISMA-ScR) 准则.
- 包括临床研究,评论和验证研究中的数据的描述性综合.
主要成果:
- 深度学习模型在神经成像解释中超过85%的准确性.
- 监督机器学习改善了自闭症谱系障碍和ADHD亚型的分类.
- 人工智能决策支持系统提高了诊断效率;生物信号人工智能识别了潜在的生理标记.
结论:
- 人工智能技术在儿科神经发育中显示出早期诊断和临床决策的巨大潜力.
- 挑战包括研究设计的变化,算法标准化和道德问题 (隐私,偏见,公平).
- 多中心验证和强大的监管框架对于临床实施至关重要.
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