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相关概念视频

Angle Closure Glaucoma: Treatment01:28

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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
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开发和验证一个可解释的基于clinlabomics的机器学习模型,用于在PPPM的背景下选初级闭角眼.

Zhuqing Li1, Jun Ren1, Jianing Wu1

  • 1Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China.

The EPMA journal
|September 15, 2025
PubMed
概括

一个新的机器学习模型使用常规血液检测结果来预测初级角闭眼光瘤 (PACG) 风险. 这种clinlabomics方法使早期查和个性化监测成为可能,改善了对玻璃眼的护理的可用性.

关键词:
在这里,我们可以看到AIAIAI.临床基因组学 临床基因组学 临床基因组学眼风险 眼风险 眼风险改善了个人结果.机器学习是机器学习.患者的分层是患者的分层.预测性预防型个性化医学 (PPPM/ 3PM)主要关闭角度的玻璃眼 (Glaucoma)这就是 SHAP SHAP 的意思.查检查 查检查 查检查

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科学领域:

  • 眼科和人工智能的人工智能
  • 使用clinlabomics数据进行预测诊断.

背景情况:

  • 主要闭角光眼 (PACG) 是不可逆转失明的主要原因.
  • 目前的查方法是反应性的,在症状出现后检测损伤,需要专门的,资源密集型成像.
  • 迫切需要早期,易于使用的查工具来预防视力丧失.

研究的目的:

  • 开发和验证基于clinlabomics的新型机器学习模型,用于PACG的早期查.
  • 将PACG风险较高的个体分层,使有针对性的眼科评估成为可能.
  • 为了与预测性,预防性和个性化医学 (PPPM/3PM) 原则对准青光眼的管理.

主要方法:

  • 这是一项多中心的回顾性研究,涉及3700多名参与者 (正常受试者和PACG患者).
  • 利用来自数字医疗记录的临床实验室数据进行模型开发和验证 (发现,内部,外部队列).
  • 对比了12个机器学习模型,使用SHAP来减少特征和模型解释,使用AUC,AUCPR,灵敏度,特异性和准确性来评估性能.

主要成果:

  • 最后一个使用六个关键特征 (TT,PDW,MCV,APTT,TC,PT) 的LightGBM (LGBM) 模型,在选PACG中显示出高精度 (AUC=0.91,精度=0.84).
  • 该模型在内部 (AUC=0.87) 和外部 (AUC=0.85) 验证队列中保持了强的性能.
  • 该模型已成功转化为可访问的Web应用程序,用于实际传播.

结论:

  • 使用常规血液参数的基于临床适用的clinlabomics模型可以有效地查PACG.
  • 该模型通过可解释的人工智能促进了早期风险识别,经济有效的人口查和个性化风险评估.
  • 这种方法通过利用常规血液检查作为预测指标,提高了对青光眼的护理的可访问性,特别是在资源有限的环境中.