目的：开发一款可自动校准测试距离的智能手机视力检测APP(WHOeye的iOS版本)，并评估其实用性。方法： WHOeyes在经过验证的视力检测APP “V@home”的基础上新增自动距离校准(automatic distance calibration, ADC)功能。研究招募了3组不同年龄(≤20岁、20~40岁、>40岁)的中国受试者，分别使用糖尿病视网膜病变早期治疗研究(Early Treatment Diabetic Retinopathy Study, ETDRS)视力表和WHOeyes进行远距离和近距离的视力检测。ADC功能用于确定WHOeyes的测试距离。红外测距仪用于确定ETDRS的测试距离以及WHOeyes的实际测试距离。通过问卷调查评估用户满意度。结果：WHOeyes ADC确定的实际测试距离在3个年龄组中均与预期测试距离总体上表现出良好的一致性(P > 0.50)。在远距离和近距离视力检测方面，WHOeyes的准确性与ETDRS相当。WHOeyes与ETDRS之间的平均视力差异范围为–0.084 ~ 0.012 log^MAR，各组的二次加权卡帕系数(quadratic weighted kappa, QWK)均大于0.75。WHOeyes在近距离和远距离视力检测中的重测信度高，平均差异范围为–0.040 ~ 0.004 log^MAR，QWK均大于0.85。问卷调查显示WHOeyes具有较好的用户体验和接受度。结论：与金标准ETDRS视力表方法相比，WHOeyes测试距离较为准确，可以提供准确的远距离和近距离视力测量结果。

Background: To develop and assess usability of a smartphone-based visual acuity (VA) test with an automatic distance calibration (ADC) function, the iOS version of WHOeyes. Methods: The WHOeyes was an upgraded version with a distinct feature of ADC of an existing validated VA testing APP called V@home. Three groups of Chinese participants with different ages (≤20, 20-40, >40 years) were recruited for distance and near VA testing using both an Early Treatment Diabetic Retinopathy Study (ETDRS) chart and the WHOeyes. The ADC function would determine the testing distance. Infrared rangefinder was used to determine the testing distance for the ETDRS, and actual testing distance for the WHOeyes. A questionnaire-based interview was administered to assess satisfaction. Results: The actual testing distance determined by the WHOeyes ADC showed an overall good agreement with the desired testing distance in all three age groups (p > 0.50). Regarding the distance and near VA testing, the accuracy of WHOeyes was equivalent to ETDRS. The mean difference between the WHOeyes and ETDRS ranged from -0.084 to 0.012 log^MAR, and the quadratic weighted kappa (QWK) values were greater than 0.75 across all groups. The test-retest reliability of WHOeyes was high for both near and distance VA, with a mean difference ranging from -0.040 to 0.004 log^MAR and QWK all greater than 0.85. The questionnaire revealed an excellent user experience and acceptance of WHOeyes. Conclusion: WHOeyes could provide accurate measurement of the testing distance as well as the distance and near VA when compared to the gold standard ETDRS chart.

人工智能(artificial intelligence, AI)在医学领域的广泛应用为探索眼部与全身健康的关系提供了新的机遇。文章回顾了眼科AI在心血管健康、神经系统健康、肾脏健康和衰老过程中的应用。在心血管健康方面，AI能够通过分析眼底图像预测心血管疾病风险因素和未来心血管事件，并提供了简便、有效的风险分层方法。在神经系统健康方面，眼科AI在阿尔茨海默病早期诊断和帕金森病识别方面显示出潜力，尽管对未来事件预测仍具挑战性。针对多发性硬化，眼科AI在诊断和预测残疾进程上展现了良好效果。在肾脏健康中，眼科AI技术通过分析视网膜图像可预测肾功能相关指标、直接检测肾病事件，展示了其在改善肾病筛查方式和减轻医疗负担方面的潜力。在衰老过程中，AI能够利用眼部图像预测生物年龄、视网膜年龄差和晶状体年龄等参数提供了生物衰老指标，为理解衰老与眼部健康的关联提供了新视角。

The widespread application of artificial intelligence (AI) in the medical field has provided new opportunities to explore the relationship between eye and whole body health. This article reviews the application of ophthalmic AI in cardiovascular health, neurological health and aging. In terms of cardiovascular health, AI can predict cardiovascular disease risk factors and future cardiovascular events by analyzing fundus images, and provides a simple and effective risk stratification method. In terms of neurological health, ophthalmic AI shows potential in early diagnosis of Alzheimer's disease and identification of Parkinson's disease, although the prediction of future events remains challenging. For multiple sclerosis, ophthalmic AI has shown good results in diagnosing and predicting the progression of disability. In kidney health, ophthalmic AI technology can predict kidney function-related parameters and detect kidney disease events by analyzing retinal images, demonstrating its potential in improving kidney disease screening methods and reducing medical burdens. In the aging process, AI can use eye images to predict biological age. Parameters such as retinal age gap and LensAge provide biological aging indicators, providing a new perspective for understanding the relationship between aging and eye health.