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.

随着智能手机覆盖率的增加与可用性的提升，实现智能健康管理的应用程序成为新兴研究热点。新一代智能手机可通过追踪步数，监测心率、睡眠，拍摄照片等方式进行健康分析，成为新的医学辅助工具。随着深度学习技术在图像处理领域的不断进展，基于医学影像的智能诊断已在多个学科全面开花,有望彻底改变医院传统的眼科疾病诊疗模式。眼科疾病的常规诊断往往依赖于各种形式的图像，如裂隙灯生物显微镜、眼底成像、光学相干断层扫描等。因此，眼科成为医学人工智能发展最快的领域之一。将眼科人工智能诊疗系统部署在智能手机上，有望提高疾病诊断效率和筛查覆盖率，改善医疗资源紧张的现状，具有极大的发展前景。综述的重点是基于深度学习和智能手机的眼病预防与远程诊疗的进展，以糖尿病性视网膜病变、青光眼、白内障3种疾病为例，讲述深度学习和智能手机在眼病管理方面的具体研究、应用和展望。

With the increasing coverage and availability of smart phones, the application of realizing intelligent health management has become an emerging research hotspot. The new generation of smart phones can perform health analysis by tracking the step numbers, monitoring heart rate and sleep quality, taking photos and other approaches, thereby becoming a new medical aid tool. With the continuous development of deep learning technology in the field of image processing, intelligent diagnosis based on medical imaging has blossomed in many disciplines, which is expected to completely change the traditional eye diseases diagnosis and treatment mode of hospitals. The conventional diagnosis of ophthalmic diseases often relies on various forms of images, such as slit lamp biological microscope, fundus imaging, optical coherence tomography, etc. As a result, ophthalmology has become one of the fastest growing areas of medical artificial intelligence (AI). The deployment of ophthalmological AI diagnosis and treatment system on smart phones is expected to improve the diagnostic efficiency and screening coverage to relieve the strain of medical resources, which has a great development prospect. This review focuses on the prevention and telemedicine progress of eye diseases based on deep learning and smart phones, taking diabetic retinopathy, glaucoma and cataract as examples to describe the specific research, application and prospect of deep learning and smart phones in the management of eye diseases.