目的：建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台，探索基于AI的医疗转诊模式，以提高协作效率和资源覆盖率。方法：训练和验证的数据集来自中国AI医学联盟，涵盖多级医疗机构和采集模式。使用三步策略对数据集进行标记: 1)识别采集模式；2)白内障诊断包括正常晶体眼、白内障眼或白内障术后眼；3)从病因和严重程度检测需转诊的白内障患者。此外，将白内障AI系统与真实世界中的居家自我监测、初级医疗保健机构和专科医院等多级转诊模式相结合。结果：通用AI平台和多级协作模式在三步任务中表现出可靠的诊断性能: 1)识别采集模式的受试者操作特征(receiver operating characteristic curve,ROC)曲线下面积(area under the curve,AUC)为99.28%~99.71%)；2)白内障诊断对正常晶体眼、白内障或术后眼，在散瞳-裂隙灯模式下的AUC分别为99.82%、99.96%和99.93%，其他采集模式的AUC均 > 99%；3)需转诊白内障的检测(在所有测试中AUC >91%)。在真实世界的三级转诊模式中，该系统建议30.3%的人转诊，与传统模式相比，眼科医生与人群服务比率大幅提高了10.2倍。结论：通用AI平台和多级协作模式显示了准确的白内障诊断性能和有效的白内障转诊服务。建议AI的医疗转诊模式扩展应用到其他常见疾病和资源密集型情景当中。

Objective: To establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. Methods: The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three step strategy: (1)capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services. Results: The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3)detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3%  of people be ’referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern. Conclusions: The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

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.

目的：分析医学人工智能通识课程“眼科人工智能的研发与应用”的开展效果，为相关医学人工智能通识课程的开展提供参考和借鉴。方法：纵向观察性研究。观察分析2020年秋季学期眼科人工智能的研发与应用通识课程学生人群，课程考核结果以及学生对课程的整体评价。结果：共有118名本科生同学参与了课程学习。其中大部分为低年级临床医学专业本科生。期中考核得分为77.21±10.07，有56位同学(47.46%)达到80分以上。期末考核得分为82.24±6.77，有91位同学(77.12%)达到80分以上。同学对课程的评分为98.76±3.55，超过90%的同学表示课程备课认真、授课条理清晰、表达准确。结论：本课程的顺利进展证明医学人工智能联合教学模式的可行性，理论和实践穿插的教学设置帮助同学们更好地掌握知识技术，完成教学目标。

Objective: To analyze the effectiveness of medical education curriculum named “Development and Application of Ophthalmic Artificial Intelligence”, and provide reference for the development of other related curriculums. Methods: Longitudinal observational study method was adopted. During the fall semester of 2020, we conducted an education curriculum named “Development and Application of Ophthalmic Artificial Intelligence” and analyzed the results of mid-term and final examinations, and curriculum evaluation of students. Results: There were 118 undergraduate students taking the course and most of them were junior students majoring in clinical medicine. The score of the mid-term examination was in the range of 77.2±10.07, and 56 students (47.46%) got more than 80 points. The score of the final examination was in the range of 82.24±6.77, and 91 students (77.12%) got more than 80 points. The score of course evaluation of students was in the range of 98.76±3.55, and more than 90% of the students thought that teachers have made full preparations before class, together with clear teaching logic and accurate expressions in class. Conclusion: The smooth progress of our course proved the feasibility of medical artificial intelligence teaching. The teaching setting interspersed with theory and practice could help students to master knowledge and technology better, so as to achieve the teaching objectives.

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.