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相关概念视频

Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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机器学习预测婴儿早产治疗视网膜病变的风险

Henry P Foote1, Yanchen J Ou2, Suchir Bhatt3

  • 1Department of Pediatrics, Duke University, Durham, North Carolina, USA.

Neonatology
|November 18, 2025
PubMed
概括

机器学习模型可以识别需要早产视网膜病变 (ROP) 治疗的婴儿,从而减少不必要的查. 这些模型为高风险婴儿的ROP检测提供了更精确的方法.

关键词:
机器学习 机器学习预测模型的预测模型.过早生育 过早生育过早生育的视网膜病变非常低的出生体重非常低的出生体重.

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科学领域:

  • 新生儿眼科新生儿眼科
  • 医疗人工智能的人工智能
  • 在医疗保健中的预测分析.

背景情况:

  • 早产视网膜病变 (ROP) 是儿童失明的主要原因.
  • 当前的ROP选指南可能过于广泛,导致不必要的评估.
  • 需要改进的模型来识别高风险的婴儿ROP.

研究的目的:

  • 开发和验证机器学习 (ML) 模型,用于预测需要ROP治疗的需求.
  • 使用ML模型,根据ROP治疗时间对婴儿进行分层.
  • 将ML模型的性能与传统的物流回归 (LR) 模型进行比较.

主要方法:

  • 使用了103,701名婴儿的多中心队列 (出生体重≤1,500克或妊娠年龄≤30周).
  • 从出生后14日至98天的2周间隔开发了ML模型,使用临床相关的变量.
  • 在25105名婴儿的单独队列中验证了模型,并将性能与LR模型进行了比较.

主要成果:

  • 28日ML模型在验证队列中表现出高于LR模型的性能 (AUROC:0.916与0.903;AP:0.190与0.160).
  • 在100%的灵敏度值下,ML模型实现了负预测值>99.9%.
  • 与目前的指导方针相比,ML模型可能会将需要查的婴儿数量减少14%.

结论:

  • ML模型在预测需要ROP治疗和分层婴儿风险方面是有效的.
  • 这些模型显示了减少不必要的ROP选的潜力.
  • 需要进一步的研究来在临床实践中实现这些基于模型的ROP预测.