The Guangzhou Twin Eye Study (GTES) is a cohort of twins living in South China that has been longitudinally followed for more than 15 years. This study has extensively investigated the heritability of myopia and the influence of environmental factors, producing significant and far-reaching impacts. GTES has found a high heritability of axial length and peripheral refraction, the significant role of education in myopia progression, and established prediction model for myopia onset and progression. The study has also explore the impact of both genetic and environmental factors on myopia development. By reviewing the major findings on myopia from the GTES, we hope to better inform public health strategies and clinical practices aimed at mitigating the global myopia epidemic.

The Guangzhou Twin Eye Study (GTES) is a cohort of twins living in South China that has been longitudinally followed for more than 15 years. This study has extensively investigated the heritability of myopia and the influence of environmental factors, producing significant and far-reaching impacts. GTES has found a high heritability of axial length and peripheral refraction, the significant role of education in myopia progression, and established prediction model for myopia onset and progression. The study has also explore the impact of both genetic and environmental factors on myopia development. By reviewing the major findings on myopia from the GTES, we hope to better inform public health strategies and clinical practices aimed at mitigating the global myopia epidemic.

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.

目的：观察不同程度早期糖尿病视网膜病变（diabetic retinopathy，DR）的多焦视网膜电图（multifocal electroretinograms，mf-ERG）一阶 kernel 反应（first order kernel，FOK）的变化特征，探讨糖尿病患者的视网膜组织形态学改变与功能学检查的关联，及早期DR视功能受损的敏感监测指标。方法：横断面研究，采用多焦视觉电生理检查系统测量分析16例无DR组、15 例轻度非增殖性DR（non-proliferative diabetic retinopathy，NPDR）组和 15 例中度非增殖性 DR（中度 NPDR 组），正常对照组 16 例 FOK 反应，选右眼测量，比较不同程度 DR 的 N1，P1 波潜伏期、振幅密度差异。结果：与正常组相比，无 DR 组、轻度 NPDR 组、中度 NPDR 组的N1，P1波潜伏期延长分别 1.42，1.35，2.75 ms和 0.98，1.01，2.71 ms（P < 0.001），N1，P1波振幅密度差异无统计学意义（P > 0.05）。随DR程度加重，N1，P1波潜伏期逐渐延长。无 DR 组虽尚未出现可见视网膜病变，但FOK 的 N1，P1 波潜伏期已出现异常。结论：糖尿病患者早期眼底尚未出现可见视网膜血管病变，mfERG 的 FOK 的 N1，P1 波潜伏期已出现异常。FOK 的 N1，P1 波的潜伏期的延长与 DR 的血管病变的严重程度呈正相关。FOK 的 N1，P1 波的潜伏期可能是较振幅密度更为敏感视网膜功能异常的观察指标。

Objective: To investigate the change of the first-order kernel (FOK) in multifocal electroretinogram (mf-ERG) in different severity grade of early diabetic retinopathy.

Methods: The FOK of mf-ERG were performed in 16 eyes with no diabetic retinopathy (non-DR group), 15 with mild non-proliferative diabetic retinopathy (mild NPDR group) and 15 with moderate NPDR; 15 age-matched healthy subjects were selected in the control group.

Results: Compared with the control group, N1 and P1 implicit times in the non-DR group, the mild NPDR group and the moderate NPDR group were delayed by 1.42, 1.35, 2.75 ms and 0.98, 1.01, 2.71 ms (all P < 0.001). N1 and P1 amplitudes did not significantly change (both P > 0.05).

Conclusion: Implicit times of N1 and P1 of mf-ERG delay before diabetic patients have no visible retinal vascular lesions. There is a significant correlation between retinopathy severity and the magnitude of the delays of N1 and P1 implicit times. Compared with the amplitude, implicit time of FOK yields high sensitivity in predicting the incidence of abnormal retinal function. Sensitivity to abnormal retinal function

目的: 探讨 ETDRS 对数视力表对儿童视力检查的可重复性及其影响的相关因素。

方法: 在流行病学调查的过程中, 随机使用 ETDRS 对数视力表, 为 250 位裸眼视力低于 0.5 和 98 位视力正常儿童进行裸眼视力重复检查。
结果: 两次视力测量之间差异的均数为0.004log^±0.07; Kappa 分析结果具有很好的一致性(k = 0.71) ; 性别与视力检查一致性无明显相关(P = 0.845) ; 年龄与视力检查一致性有显著相关性(P = 0.019) , 年龄越小视力检查一致性越差; 屈光不正与视力检查一致性也有显著相关性(P = 0.000) , 近视度数在- 1.00D～- 5.00D 之间的儿童视力检查一致性相对差, 而正视眼的视力检查一致性较好。

结论: 结果提示 ETDRS 对数视力表适合儿童视力检查, 建议推广使用。

Purpose: To evaluate repeatability of the ETDRS log MAR visual acuity measurementin children and the relative influence factors.

Methods: The children (n = 348) with visual acuity less than 0.5 ( equal to 0.3 logunit) in either eye,or one tenth in children with normal visual acuity were chosen todo repeatable uncorrected VA measurement with Bland-Altman analysis and Kappa analysis using ETDRS acuity chart.

Results: The mean difference of visual acuity was 0.004log^±0.07. There was a significant repeatability (k = 0.71) between two visual acuity examination. There were significant consistent results both on male and female patients (P = 0.845) . A significant relationship was found between age and VA repeatability(P = 0.019) . The VA repeatability could also be influenced by refractive error (P = 0.000) . The acuity measurement in children with emmetropia (k = 0.82) had a higher repeatability than in the children with myopia (k = 0.66) .

Conclusions: The ETDRS visual acuity chart can provide a repeatable measure of visual acuity in children. It is recommended for clinic examination of children. 

目的：探索基于眼底彩照和人工智能构建冠心病智能诊断系统的可行性。方法：于2013—2014年收集广东省人民医院530例患者共2117张眼底彩照，其中冠心病217例共909张眼底彩照。根据患者有无冠心病的情况进行标记，使用Inception-V3深度卷积神经网络训练人工智能模型，随后使用验证数据判断模型的准确率。计算深度卷积网络模型的准确性、一致率、敏感性、特异性和受试者工作特性曲线下面积(area under the curve，AUC)。结果：在2117张眼底彩照中，1903张用于模型训练，214张用于模型的性能评估。在测试集中，该算法的准确性为98.1%，一致率为98.6%，敏感性为100.0%，特异性为96.7%，AUC为0.988(95%CI：0.974~1.000)。结论：眼底彩照联合人工智能技术可精准判定冠心病，该模型具备较高的敏感性和特异性，但须进一步增加样本量，使用大样本量数据验证该模型，排除过拟合的可能性。

Objective: To explore the feasibility of developing a deep learning algorithm for detecting coronary heart diseases based on fundus color photography and artificial intelligence (AI). Methods: A total of 2 117 fundus  color photographs were taken from 530 patients in Guangdong Provincial People’s Hospital from 2013 to 2014,including 909 fundus color photographs from 217 patients with coronary heart disease (CHD). According to whether the patient had coronary heart disease or not, the Inception-V3 depth convolution neural network was used to train the deep learning model, and then the validation data were used to judge the accuracy of the model. The accuracy, consistency rate, sensitivity and specificity of the deep convolution network model and the area under the working characteristic curve (AUC) were calculated. Results: Among the 2 117 fundus color photographs, 1 903 were used for model training, and 214 were used to test the accuracy of the model. In the test dataset, the accuracy of the algorithm was 98.1%, the consistency rate was 98.6%, the sensitivity was 100.0%, and the specificity was 96.7%. The AUC was 0.988 (95% CI, 0.974–1.000). Conclusion: The combination of fundus color photography and artificial intelligence can achieve the accurate diagnosis of the coronary heart disease, and the model has high sensitivity and specificity. However, future studies are warranted to validate our model and exclude the possibility of over-fitting.

目的：探索基于眼底彩照和人工智能构建冠心病智能诊断系统的可行性。方法：于2013—2014年收集广东省人民医院530例患者共2 117张眼底彩照，其中冠心病217例共909张眼底彩照。根据患者有无冠心病的情况进行标记，使用Inception-V3深度卷积神经网络训练人工智能模型，随后使用验证数据判断模型的准确率。计算深度卷积网络模型的准确性、一致率、敏感性、特异性和受试者工作特性曲线下面积(area under the curve，AUC)。结果：在2 117张眼底彩照中，1 903张用于模型训练，214张用于模型的性能评估。在测试集中，该算法的准确性为98.1%，一致率为98.6%，敏感性为100.0%，特异性为96.7%，AUC为0.988(95%CI：0.974~1.000)。结论：眼底彩照联合人工智能技术可精准判定冠心病，该模型具备较高的敏感性和特异性，但须进一步增加样本量，使用大样本量数据验证该模型，排除过拟合的可能性。

Objective: To explore the feasibility of developing a deep learning algorithm for detecting coronary heart diseases based on fundus color photography and artificial intelligence (AI). Methods: A total of 2 117 fundus color photographs were taken from 530 patients in Guangdong Provincial People’s Hospital from 2013 to 2014, including 909 fundus color photographs from 217 patients with coronary heart disease (CHD). According to whether the patient had coronary heart disease or not, the Inception-V3 depth convolution neural network was used to train the deep learning model, and then the validation data were used to judge the accuracy of the model. The accuracy, consistency rate, sensitivity and specificity of the deep convolution network model and the area under the working characteristic curve (AUC) were calculated. Results: Among the 2 117 fundus color photographs, 1 903 were used for model training, and 214 were used to test the accuracy of the model. In the test dataset, the accuracy of the algorithm was 98.1%, the consistency rate was 98.6%, the sensitivity was 100.0%, and the specificity was 96.7%. The AUC was 0.988 (95% CI, 0.974–1.000). Conclusion: The combination of fundus color photography and artificial intelligence can achieve the accurate diagnosis of the coronary heart disease, and the model has high sensitivity and specificity. However, future studies are warranted to validate our model and exclude the possibility of over-fitting.