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人工智能在新生儿胃肠道学和营养学中的应用

Wissam Shalish¹, Josef Neu², Guilherme Sant'Anna³

¹Department of Pediatrics, Division of Neonatology, Montreal Children's Hospital, McGill University Health Center, 1001 Boul. Décarie, Room B05.2714, Montreal, Quebec, H4A 3J1, Canada.

Seminars in fetal & neonatal medicine

|November 25, 2025

概括

人工智能和机器学习通过实现精确诊断和个性化营养来增强新生儿护理. 这些技术有助于识别营养缺陷和胃肠道疾病,以改善婴儿的结果.

科学领域:

新生儿医学新生儿医学
计算生物学是一种计算生物学.
胃肠病学胃肠病学

背景情况:

优化营养和诊断新生儿的胃肠道疾病是传统方法的挑战.
婴儿的个性化需求,特别是早产和满产婴儿的个性化需求,往往没有得到当前指导方针的满足.

研究的目的:

探索人工智能 (AI) 和机器学习 (ML) 在新生儿护理中的应用.
为突出精确诊断,个性化治疗和婴儿营养优化的进展.

主要方法:

利用婴儿风险因子和代谢概况聚类的多组数据.
采用机器学习来重新定义诸如死性肠球炎等疾病.
开发数字双胞胎模型,用于实时营养管理.

主要成果:

机器学习将死性肠球炎重新归类为肠道损伤的频谱.
数字双胞胎模型显示了个性化,实时营养优化的潜力.
将先进的监测与生物标志物和传感器相结合,可以提高早期检测.

结论:

数字技术为新生儿营养和胃肠道疾病诊断提供了新的方法.
人工智能和机器学习可以识别营养缺乏和胃肠道病理的早期预测因素.
基于这些进展的积极干预措施可能会改善新生儿的结果.

关键词:

人工智能的人工智能是人工智能.机器学习是机器学习.死性肠道结肠炎 (NECO) 是一种新生儿营养新生儿营养精准医学是一门精准的医学.

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