人类胎儿的功能神经行为:使用4D超声波和AI进行产前评估的综合框架
Julian Dewantiningrum1, Wiku Andonotopo2, Muhammad Adrianes Bachnas3
1Fetomaternal Division, Department of Obstetrics and Gynecology, Medical Faculty of Diponegoro University, Dr. Kariadi Hospital, Semarang, Indonesia.
Journal of perinatal medicine
|January 27, 2026
概括
使用四维超声 (4D-US),评分系统和AI进行产前神经行为评估,可以识别神经发育的脆弱性. 这些方法是可行的,可重复的,在临床上具有信息性,用于早期检测和改善护理.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 发育儿科 发育儿科
背景情况:
- 产前神经行为评估对于早期识别神经发育脆弱性至关重要.
- 四维超声 (4D-US) 提供了先进的胎儿行为可视化.
- 结构化评分系统和人工智能 (AI) 是客观分析的新兴工具.
研究的目的:
- 审查关于4D-US,评分系统 (如KANET,GMA) 和人工智能用于产前神经行为评估的证据.
- 为早期识别神经发育风险制定一个综合框架.
- 评估这些方法的可行性,可重复性和临床实用性.
主要方法:
- 根据PRISMA原则,从2000年至2025年进行叙事文献综述.
- 包括使用3D/4D超声波用于胎儿神经行为 (运动活动,面部表情,过渡) 的研究.
- 检查KANET,产前GMA和基于AI的行为分析;使用纽卡斯尔-太华尺度和JBI检查清单进行质量评估.
主要成果:
- 胎儿运动模式和面部表情显示与神经电路发育相关的层次成熟.
- KANET 证明了可重现的评分和与新生儿结局的相关性,特别是在高风险怀孕中.
- 人工智能分类器客观量化胎儿活动,支持自动评估.
结论:
- 通过4D-US,结构化工具和AI进行功能神经行为评估是可行的和有信息的.
- 行为标记,神经声学和计算建模的整合可以增强早期神经风险检测.
- 这种方法有望改善长期神经发育护理途径.
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