目的：评估白内障人工智能辅助诊断系统在社区筛查中的应用效果。方法：采用前瞻性观察性研究方法对白内障人工辅助诊断系统的应用效果进行分析，结合远程医疗的模式，由社区卫生人员对居民进行病史采集、视力检查和裂隙灯眼前节检查等，将数据上传至云平台，由白内障人工智能辅助诊断系统和人类医生依次进行白内障评估。结果：受检人群中男性所占比例为35.7%，年龄中位数为66岁，裂隙灯眼前节照片有98.7%的图像质量合格。该白内障人工智能辅助诊断系统在外部验证集中检出重度白内障的曲线下面积为0.915。在人类医生建议转诊的病例中，有80.3%也由人工智能系统给出了相同的建议。结论：该白内障人工智能辅助诊断系统在白内障社区筛查的应用中具有较好的可行性和准确性，为开展社区筛查疾病提供了参考依据。
Objective: To evaluate the effectiveness of an artificial intelligence-assisted diagnostic system for cataract screening in community. Methods: A prospective observational study was carried out based on a telemedicine platform. Patient history, medical records and anterior ocular segment images were collected and transmitted from community healthcare centers to Zhongshan Ophthalmic Center for evaluation by both ophthalmologists and artificial intelligence-assisted cataract diagnostic system. Results: Of all enumerated subjects, 35.7% were male and the median age was 66 years old. Of all enumerated slit-lamp images, 98.7% met the requirement of acceptable quality. This artificial intelligence-assisted diagnostic system achieved an AUC of 0.915 for detection of severe cataracts in the external validation dataset. For subjects who were advised to be referred to tertiary hospitals by doctors, 80.3% of them received the same suggestion from this artificial intelligence-assisted diagnostic system.Conclusion: This artificial intelligence-assisted cataract diagnostic system showed high applicability and accuracy in community-based cataract screening and could be a potential model of care in community-based disease screening.
建立标准化的数据中心有利于收集高质量数据资源与促进医学人工智能的发展，在医疗大数据的基础上建立不同应用场景的医疗人工智能系统，整合、搭建可满足多种疾病诊疗需求的智能服务云平台，全面提升智能医疗管理的效率。本文以眼科为研究基础，对眼科数据中心和智能服务云平台的建设经验进行总结分析，为眼科及其他专科开展人工智能研究、建立数据中心、搭建智能服务云平台等方面提供参考。
The establishment of standardized data center can promote the accumulation of high-quality data resources and the development of medical artificial intelligence. On the basis of medical big data, medical artificial intelligence systems in different application scenarios can be established and integrated into an intelligent service cloud platform, which improves the management efficiency of intelligent medical systems. This article takes ophthalmology as a prototype to summarize the experience of the establishment of ophthalmic data center and intelligent service cloud platform, aiming to provide reference and guidance for ophthalmology and other specialties to carry out artificial intelligence research, establish data center and build an intelligent service cloud platform.
目的：评价StarEyes 900(万灵帮桥，中国)与IOLMaster 500(蔡司，德国)2种眼科光学生物测量仪测量健康受试者眼部参数的差异性、相关性及一致性。方法：前瞻性观察2021年6月至7月于中山大学中山眼科中心进行眼部检查的62例健康受试者共124只眼，分别通过StarEyes 900与IOLMaster 500完成眼轴长度(axial length，AL)、最小角膜屈光力径线上角膜曲率(keratometry for the flattest meridian，Kf)、最大角膜屈光力径线上角膜曲率(keratometry for the steepest meridian，Ks)、平均角膜曲率(mean keratometry，Km)、角膜白到白直径(white-to-white corneal diameter，WTW)等参数的测量，采用配对t检验、Pearson相关分析和Bland-Altman法对其测量结果的差异进行评价。结果：StarEyes 900与IOLMaster 500测量的AL分别为(24.18±1.08) mm和(24.16±1.08) mm；Kf分别为(42.84±1.65) D和(43.04±1.57) D；Ks分别为(44.34±1.90) D和(44.17±1.80) D；Km分别为(43.59±1.73) D和(43.61±1.64) D；WTW分别为(11.64±0.29) mm和(11.64±0.30) mm。StarEyes 900与IOLMaster 500在测量Km、WTW时，其差异无统计学意义(P>0.05)，而在AL、Kf、Ks的测量上差异有统计学意义(P<0.01)。其中StarEyes 900所测的AL和Ks值大于IOLMaster 500，而Kf、Km和WTW值则小于IOLMaster 500。经Pearson相关分析，2种仪器的测量结果均表现出较高的相关性；经Bland-Altman法评价，2种仪器的测量结果均表现出较高的一致性。结论：StarEyes 900与IOLMaster 500测量的Km、WTW均表现出较高的一致性，2种方法可互为参考；测量的AL、Kf、Ks存在的差异具有统计学意义；各项参数的测量均具有较好的相关性和一致性。
Objective: To evaluate the difference, correlation and agreement of eye parameters measured by StarEyes 900 visual function analyzer (Wan Ling Bang Qiao, China) and IOLMaster 500 (Carl Zeiss, Germany) swept-source optical coherence tomography biometer. Methods: A prospective study was designed involving 62 healthy subjects (124 eyes) undergoing ophthalmic examinations in Zhongshan Ophthalmic Center from June 2021 to July 2021. Data from their both eyes were selected for analysis in all patients. Axial length (AL), keratometry for the steepest meridian (Ks), keratometry for the flattest meridian (Kf), mean keratometry (Km) and corneal diameter (WTW) were measured by the StarEyes 900 visual function analyzer and IOLMaster 500 swept-source optical coherence tomography biometer. A paired t-test was used to analyze the differences in measurement results. The Pearson correlation coefficient was used to analyze the correlation. Bland-Airman method was used to assess the agreement of the instruments. Results: The AL, Kf, Ks, Km and WTW obtained by StarEyes 900 and IOLMaster 500 were (24.18±1.08) mm and (24.16±1.08) mm, (42.84±1.65) D and (43.04±1.57) D, (44.34±1.90) D and (44.17±1.80) D, (43.59±1.73) D and (43.61±1.64) D, and (11.64±0.29) mm and (11.64±0.30) mm, respectively. The Km and WTW of the two devices showed no significant difference (P>0.05), while the AL, Ks and Kf showed significant differences (all P<0.01). The AL and Ks obtained by StarEyes 900 were higher than by IOLMaster 500, while the Kf, Km and WTW were lower. The measurements of five aforementioned biometric parameters by both devices showed good correlation by Pearson correlation coefficient and good agreement by Bland-Airman. Conclusion: The Km and WTW measured by the two devices showed no significant difference, and provided references to one another. The difference in AL, Kf and Ks between the two devices showed significant differences. All of the measurements showed good correlation by Pearson correlation coefficient and good agreement by Bland-Airman.
