PDF(Pubmed)
Abstract：
The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.
摘要：
在管理圆锥角膜方面取得了令人瞩目的最新进展，最常见的角膜扩张症，鼓励研究人员对这种疾病进行进一步的研究。尽管有大量关于圆锥角膜的信息,关于发现轻度病例的辩论仍在继续。早期检测在促进侵入性较小的治疗中起着至关重要的作用。这篇综述涵盖了从基础科学到人工智能在圆锥角膜患者中的应用的角膜数据。诊断系统利用自动决策树，支持向量机，和各种类型的神经网络，结合各种角膜成像设备的输入。尽管将人工智能技术集成到角膜成像设备中可能需要时间，它们在临床实践中的受欢迎程度越来越高。本文回顾的大多数研究表明，正常和圆锥角膜病例之间具有很高的辨别能力，对亚临床圆锥角膜的判别力相对较低。
