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基于学习的模型预测IVIG抵抗和冠状动脉病变在川崎病:技术方面和研究特征的审查

Danilo Mirata¹, Anna Chiara Tiezzi¹, Lorenzo Buffoni²

¹Pediatric Department, School of Sciences of Human Health, University of Florence, Florence, Italy.

Paediatric drugs

|April 3, 2025

概括

人工智能在预测儿童血管炎的Kawasaki病 (KD) 的结果方面表现有希望. 然而,当前的人工智能模型需要更多的数据和验证,以便在确定高风险患者和冠状动脉病变时可靠的临床使用.

科学领域:

儿科风湿病学儿科风湿病学
医疗信息学医疗信息学
人工智能在医学中的应用

背景情况:

川崎病 (KD) 是获得性儿科心脏病的主要原因,冠状动脉病变 (CALs) 是严重的并发症.
早期识别高风险的KD患者,特别是那些抵抗初始治疗的患者,对于个性化治疗至关重要.
目前对KD的预后评分系统的可靠性有限,这引发了对先进预测模型的兴趣.

研究的目的:

审查人工智能 (AI) 在Kawasaki病患者分层中的近期应用.
专注于AI模型在预测静脉注射免疫球蛋白 (IVIG) 耐药性和CALs风险方面的能力.
评估人工智能的潜力,以改善患者分层,并指导KD的向治疗策略.

主要方法:

在2019年1月至2024年4月期间发表的关于使用基于人工智能的KD预测模型的研究的叙述性综述.
对21篇符合条件的论文进行分析,重点关注研究设计,患者人口统计和AI模型性能.
选择研究的技术和统计审查,注意到方法和数据参数的异质性.

主要成果:

人工智能模型显示了预测IVIG耐药性的潜力,这是KD中CAL风险的关键因素.
大多数审查的研究 (90%) 来自亚洲医院,主要是回顾性 (85.7%),许多研究的患者数量少于1000人.
只有少数人工智能模型实现了高灵敏度 (>80%) 的预测,很少有人提供算法/数据集访问.

结论:

人工智能为改善KD患者分层和预测CALs等严重并发症提供了一个有希望的途径.
仍然存在重大挑战,包括小样本大小,数据异质性和需要多中心验证,限制目前的临床适用性.
提高人工智能模型的有效性需要更大,高质量的数据集,改进的标签,对算法的开放访问,以及为了可重复性和改进而进行协作数据共享.

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