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基于人工智能的算法的应用在预测角的进展:一个系统的审查

Hassan Hashemi¹, Alireza Jamali², Payam Nabovati²

¹Noor Ophthalmology Research Center, Noor Eye Hospital, Tehran, Iran. hhashemi@noorvision.com.

International ophthalmology

|November 15, 2025

概括

人工智能 (AI) 显示出预测形 (KCN) 进展的潜力. 然而,需要进一步的研究来标准化定义和验证AI模型在不同的患者群体临床使用.

科学领域:

眼科医生眼科眼科
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

角膜 (KCN) 是一种影响角膜形状的渐进性眼睛疾病.
预测KCN进展对于及时干预和管理至关重要.
目前用于预测KCN进展的方法存在局限性.

研究的目的:

系统地审查对人工智能 (AI) 算法的研究,以预测角 (KCN) 的进展.
评估AI在KCN进展预测中的有效性和局限性.

主要方法:

在PubMed,谷歌学者和ScienceDirect的系统文献综述.
包括使用人工智能算法评估KCN变化的研究,以英语出版.
使用 QUADAS-2 工具进行质量评估,并对发现进行叙事综合.

主要成果:

十项研究 (10,940只眼睛) 分析了人工智能模型 (机器学习,深度学习) 的KCN进展.
人工智能模型显示了中度至高的区分能力 (AUC 0.77-0.93,准确度为77.5%-84.9%).
关键预测因素:后部升高,Kmax,年龄较小;由临床因素 (IgE,眼部摩擦,维生素缺乏) 增强.

结论:

人工智能模型在识别KCN进展方面表现有前途.
限制包括不一致的进展标准,缺乏外部验证和设备依赖.
标准化定义和多中心验证对于临床整合至关重要.

关键词:

人工智能的人工智能角膜成像角膜成像深度学习 (Deep Learning) 是一种深度学习.克拉托科努斯 (Keratoconus) 是一种类型的角质球.机器学习机器学习预测进展的预测.

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