使用子宫活动来预测人工智能辅助模型的早产:一个叙述性的系统性审查
Jagrati Chopra1,2,3, Charlotte Maden1,2,3, Isabel Howlett4
1The National Institute for Health and Care Research (NIHR), London, UK.
概括
人工智能 (AI) 在使用子宫收缩分析预测早产 (PTB) 方面表现有前途. 然而,有限的外部验证和高偏差风险限制了这些人工智能模型目前用于PTB预测的适用性.
科学领域:
- 产科和妇科 产科和妇科
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 过早分娩 (PTB) 对新生儿构成重大风险,需要有效的预测方法.
- 在管理PTB时,平衡干预效益与胎儿和母亲的风险至关重要.
- 人工智能 (AI) 提供了分析子宫收缩的潜力,以预测PTB.
研究的目的:
- 系统地审查和评估人工智能模型的预测准确性和适用性,使用子宫收缩数据进行PTB预测.
- 评估各种人工智能方法的性能,包括深度学习和机器学习,用于分析电动造图 (EHG) 和时间序列数据用于PTB预测.
主要方法:
- 在遵循PRISMA指南的基础上,对Embase,Medline,Pubmed和Web of Science数据库进行了系统的文献审查.
- 纳入了分析电图 (EHG) 或时间序列数据的研究,使用人工智能方法 (深度学习,机器学习,神经网络) 进行PTB预测.
- 收集了人工智能模型性能,有效性和适用性的数据,并使用PROBAST框架进行了偏差评估.
主要成果:
- 总共有53个记录被纳入,其中18个检查了EHG特征,22个评估了AI分类器.
- 研究报告了38.8% (EHG特征) 和86.3% (AI分类器) 的优秀分类性能 (ACC和/或AUC≥0.9).
- 非线性EHG特征和深度学习模型表现出优异的性能,但86.7%的研究由于数据不平衡和样本规模小等问题而存在不清楚或高偏差风险.
结论:
- 非线性特征和深度学习模型显示了提高PTB预测准确性的巨大潜力.
- 目前人工智能模型用于使用子宫收缩预测PTB的适用性是有限的,因为缺乏外部验证和偏差的高风险.
- 未来的研究应该专注于将临床数据整合到高质量的研究中,并使用验证的AI模型强调早期PTB检测.
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