Interpretation of topographical maps used to detect corneal ectasias requires a high level of expertise. Several artificial intelligence (AI) technologies have attempted to interpret topographic maps. The purpose of this study is to provide a review of AI algorithms in corneal topography from the perspectives of an eye care professional, a biomedical engineer, and a data scientist. A systematic literature review using Web of Science, Pubmed, and Google Scholar was performed from 2010 to 2020 on themes regarding imaging modalities, their parameters, purpose, and conclusions and their samples and performance related to AI in corneal topography. We provide a comprehensive summary of advances in corneal imaging and its applications in AI. Combined metrics from the Dual Scheimpflug and Placido device could be a good starting point to try AI models in corneal imaging systems. The range of area under the receiving operating curve for AI in keratoconus detection and classification was from 0.87 to 1, sensitivity was from 0.89 to 1, and specificity was from 0.82 to 1. A combination of different types of AI applications to corneal ectasia diagnosis is recommended.

解释用于检测角膜扩张的地形图需要高水平的专业知识。几种人工智能 (AI) 技术已尝试解释地形图。本研究的目的是从眼保健专业人员、生物医学工程师和数据科学家的角度对角膜地形图的 AI 算法进行回顾。从 2010 年到 2020 年，使用 Web of Science、Pubmed 和 Google Scholar 对有关成像模式、其参数、目的和结论及其样本和与角膜地形图 AI 相关的性能进行了系统的文献综述。我们全面总结了角膜成像的进展及其在人工智能中的应用。Dual Scheimpflug 和 Placido 设备的综合指标可能是在角膜成像系统中尝试 AI 模型的良好起点。AI 在圆锥角膜检测和分类中的接收操作曲线下面积范围为 0.87 至 1，灵敏度为 0.89 至 1，特异性为 0.82 至 1。不同类型的 AI 应用于角膜扩张诊断的组合是推荐的。