阿尔茨海默病(Alzheimer’s disease，AD)是发生于老年期或老年前期的中枢神经系统退行性病变，以进行性认知功能障碍为特征。随着社会老龄化加剧，AD已成为全球公共卫生问题，亟需研发更敏感、便捷和经济的筛查技术进行早期防控。眼球运动与认知功能密切相关，且眼球运动检查有非侵入性、成本低、检查时间短等优点。研究眼球运动异常和认知功能障碍之间的相关性，有助于研发更简便易操作的认知功能障碍筛查工具。随着人工智能技术的发展，机器学习算法强大的特征提取和计算能力对处理眼球运动检查结果有显著优势。本文对既往AD患者与眼球运动异常之间的相关性研究进行综述，并对机器学习算法模型辅助下，基于眼球运动异常模式进行认知功能障碍早期筛查技术开发的研究前景予以展望。

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system that occurs in old age or early old age. It is characterized by progressive cognitive dysfunction. With the world population aging, AD has become a global public health problem. The development of a more sensitive, convenient, and economic screening technology for AD is urgently needed. The eye movement function is closely related to cognitive function. Moreover, eye movement examination has advantages including non-invasiveness, low cost, and short examination time. Researches on the correlation between abnormal eye movement and cognitive dysfunction can help to develop a simple and easy-to-use screening tool for cognitive dysfunction. With the development of artificial intelligence technology, the dominant feature extraction and computing capabilities of machine learning algorithms have a significant advantage in processing eye movement inspection results. This article reviews the correlation between AD and eye movement abnormalities aiming to provide the research prospects of early screening technology development for cognitive dysfunction based on abnormal eye movement with the application of machine learning models.

手术前常规检查在临床诊疗中被广泛应用，但在一些低风险择期手术前对患者进行常规检查，对提高医疗质量并无帮助，反而降低了医疗效率，增加了医疗费用。为提高效率，一些地区、机构和专家学者陆续通过宣传教育、发表共识、制定指南等方式控制无指征术前常规检查，但效果仍依赖于执业者的重视程度和专业水平。大数据机器学习方法以其标准化、自动化的特点为解决这一问题提供了新的思路。在回顾已有研究的基础上，我们抽取2017至2019年在中山大学中山眼科中心进行眼科手术的3.4万名患者的病史和体格检查资料大数据，涵盖年龄、性别等口学信息，诊断、既往疾病等病史信息，视功能、入院时身体质量指数(BMI)等体格检查信息。并以此为基础使用机器学习方法预测术前胸部X线检查是否存在异常，受试者操作特性曲线(receiver operating characteristic curve，ROC)曲线下面积达到0.864，预测准确率可达到81.2%，对大数据机器学习精简术前常规检查的新方式进行了先期探索。

Preoperative routine tests are widely prescribed in clinical settings. However, these tests do not help improving the quality of medical care in low-risk elective surgery. Instead, they are associated with lower efficiency and increasing fees. To improve the efficiency, many regions, institutions, and scholars have attempted to reduce preoperative routine tests without indications through propaganda, education, consensus, and guidelines. Nevertheless, the effects are still highly dependent on the expertise and emphasis of practitioners. Machine learning based on big data provide a new solution with its standardization and automation. Through literature review, we extracted the big data, including demographic features such as sex and age, histories including diagnosis and chronic diseases, and physical examination features such as visual function and body mass index. A total of 34 000 patients undergone ocular surgeries in Zhongshan Ophthalmic Center, Sun Yat-sen university from 2017 to 2019. Machine learning was adopted to predict the risk of finding abnormalities in chest X-ray examination, with an accuracy of 81.2%. Area under the Receiver Operating Characteristic curve was 0.864. The study could be an early exploration into the field of simplifying preoperative tests by machine learning.