Abstract In recent years, the prevention and treatment of eye diseases in children has become one of the worldwide recognized public health issues. Both genetic and environmental factors can influence the development of offspring visual systems, leading to abnormal vision acuity such as refractive errors, strabismus, and nystagmus. With the aim of elucidating the influencing factors and mechanisms of early life adverse exposures on offspring vision outcomes, the article summarizes the research on exposure in early life and visual acuity in offspring from different perspectives, including tobacco exposure during pregnancy, exposure to microelements, gestational diabetes, gestational hypertension, and drug abuse during pregnancy, so as to provide scientific evidence for the development of more effective strategies to prevent and control eye diseases in children and adolescents.

Population based health data collection and analysis are important in epidemiological research. In recent years, with the rapid development of big data, Internet and cloud computing, artificial intelligence has gradually attracted attention of epidemiological researchers. More and more researchers are trying to use artificial intelligence algorithms for genome sequencing and medical image data mining, and for disease diagnosis, risk prediction and others. In recent years, machine learning, a branch of artificial intelligence, has been widely used in epidemiological research. This paper summarizes the key fields and progress in the application of machine learning in epidemiology, reviews the development history of machine learning, analyzes the classic cases and current challenges in its application in epidemiological research, and introduces the current application scenarios and future development trends of machine learning and artificial intelligence algorithms for the better exploration of the epidemiological research value of massive medical health data in China.