随着人工智能(artificial intelligence，AI)技术的快速发展，基于深度学习(deep learning，DL)和机器学习的AI技术在医学领域上的应用受到了广泛的关注。AI在眼科的应用也逐渐向更全面更深入的层次发展，通过角膜断层扫描、光学相干断层扫描、裂隙灯图像等技术，AI在对角膜病变、结膜病变、白内障、青光眼等眼部疾病的诊断和治疗方面都表现出了良好的性能。然而AI在眼科的应用方面也存在一些诸如结果可解释性的欠缺、数据集标准化的缺乏、数据集质量的不齐、模型适用性的不足和伦理问题等挑战。在5G和远程医疗飞速发展的时代，眼科AI同时也有许多新的机遇。本文综述了AI在前段眼科疾病中的应用、临床实施的潜在挑战和前景，为AI在眼科领域的进一步发展提供参考信息。

With the rapid development of artificial intelligence (AI) technology, the application of AI technology based on deep learning (DL) and machine learning (ML) in the medical field has received widespread attention. The application of AI in ophthalmology is gradually being shifted to a more comprehensive and in-depth level. Trained on corneal tomography, optical coherence tomography (OCT), slit-lamp images, and other techniques. AI can achieve robust performance in the diagnosis and treatment of corneal lesions, conjunctival lesions, cataract, glaucoma and other ophthalmic diseases. However, there are also some challenges in the application of AI in ophthalmology, including the lack of interpretability of results, lack of standardization of data sets, uneven quality of data sets, insufficient applicability of models and ethical issues. In the era of 5G and telemedicine, there are also many new opportunities for ophthalmic AI. In this review, we provided a summary of the state-of-the-art AI application in anterior segment ophthalmic diseases, potential challenges in clinical implementation and its development prospects, and provides reference information for the further development of artificial intelligence in the field of ophthalmology.

目的：分析喉罩在肥胖患者眼科日间手术中的应用特点。方法：选择 2021 年 1 月— 2024 年 3 月在中山大学中山眼科中心行日间眼科手术 且体质量指数（body mass index，BMI）≥25 kg/m²的男性患者进行分析，将患者分为2组：超重组（BMI 25~<30 kg/m2）及肥胖组（BMI≥30 kg/m²）。比较两组患者入室时（T₁）、诱导后（T₂）、喉罩置入后（T₃）、手术开始时（T₄）、手术开始10 min（T₅）、手术结束时（T₆）、入麻醉恢复室（post-anesthesia care unit，PACU）（T₇）、喉罩拔除时（T₈）的血流动力学和血氧饱和度变化情况；并分析喉罩置入后（T₃）、手术开始时（T₄）、手术开始10 min（T₅）、手术结束时（T₆）和喉罩拔除时（T₈）患者的呼吸参数变化情况，记录两组患者的喉罩通气成功率及复苏室的停留时间。结果：纳入 118 例患者，其中超重组 57 例、肥胖组 61 例。两组均未出现喉罩通气失败病例，肥胖组在T₃、T₄、T₅、T₆以及T₈等时点气道峰压均高于超重组（P < 0.05）；肥胖组的呼吸道不良事件发生率高于超重组（P < 0.05）。两组患者围术期生命体征平稳（P > 0.05），复苏室停留时间在组间比较差异无统计学意义（P > 0.05）且均顺利日间手术离院。结论：喉罩通气下全身麻醉能安全应用在肥胖患者眼科日间手术中，高BMI与高气道峰压相关。

Objective: General anesthesia using laryngeal mask airway (LMA) ventilation can facilitate ambulatory ophthalmic surgery, however, there remains debates about the use of LMA for obese patients. This study retrospectively analyzed the application of LMA in obese patients for ambulatory ophthalmic surgery. Methods: In this retrospective study, we analyzed all patients with a BMI≥25 kg/m2 who underwent ambulatory ophthalmic surgery under general anesthesia at the Zhongshan Ophthalmic Center of Sun Yat-sen University from January 2021 to March 2024.  The patients were divided into two groups: overweight group (BMI 25~<30 kg/m2) and obese group (BMI ≥30 kg/m2) . We compared the changes in hemodynamics and oxygen saturation at the several time points, baseline after entering operating room (T₁), anesthesia induction (T₂), LMA insertion (T₃), start of surgery (T₄), 10 minutes after surgery (T₅), surgery completion (T₆), transfer to PACU (T₇), and LMA removal (T₈);and analyzed the changes in respiratory parameters of the patient at T₃, T₄, T₅, T₆, and T₈ time points between both groups. We also observed the differences in success rate of LMA insertion and ventilation and PACU stay time as well as time to discharge home between two groups. Results: 118 cases were found suitable for the final analysis and divided into overweight group (n=57) and obese group (n=61). All patients in the two groups uneventfully completed the ophthalmic ambulatory surgery and discharged home under the laryngeal mask airway ventilation, none of patients in both group displayed LMA ventilation failure. The hemodynamics and oxygen saturation in both groups were stable during perioperative period(P > 0.05). The peak airway pressures in obese group were dramatically increased than in overweight group at T₃, T₄, T₅, T₆ and T8 time points (P < 0.05), and the incidences of adverse respiratory events in obese group were significant higher than in overweight group （P < 0.05）. The PACU stay time was comparable in both groups (P > 0.05). Conclusion: LMA can be safely used for obese patients undergoing ophthalmic surgery, high BMI is related to high peak airway pressures.

人工智能(artificial intelligence，AI)在白内障手术中的应用越来越广泛，二者结合对于白内障手术的术前诊断和分级管理、术中人工晶状体选择、位置预测及术后管理(视力预测、并发症预测及随访)、手术培训和教学方面均起到巨大的促进作用。诚然，AI在与白内障手术相关的管理、分析和研究中还面临着许多问题，但其广泛的应用前景不可忽视。现对AI在白内障手术治疗和教学中的应用做以总结，并对其未来的发展做出展望。

Artificial intelligence (AI) has been widely used in cataract surgery. The combination of the two can play a great role in improving preoperative diagnosis, grading management of cataract surgery, intraoperative intraocular lens selection and location prediction, postoperative management (vision prediction, complication prediction and follow-up), surgical training and teaching. It is true that AI still faces many problems in the management, analysis and research related to cataract surgery, but its broad application prospects cannot be ignored. This review summarizes the application of AI in cataract surgery and teaching, and the future prospects of AI.

近年来，眼科人工智能(artificial intelligence，AI)迅猛发展，眼底影像因易获取及其丰富的生物信息成为研究热点，眼底影像的AI分析在眼底影像分析中的应用不断深入、拓展。目前，关于糖尿病性视网膜病变(diabetic retinopathy，DR)、年龄相关性黄斑变性(age-related macular degeneration，AMD)、青光眼等常见眼底疾病的临床筛查、诊断和预测已有较多AI研究，相关成果已逐步应用于临床实践。除眼科疾病以外，探究眼底特征与全身各种疾病之间的关系并据此研发AI诊断系统已经成为当下的又一热门研究领域。AI应用于眼底影像分析将改善医疗资源紧缺、诊断效率低下的情况，为多种疾病的筛查和诊断开辟“新赛道”。未来眼底影像AI分析的研究应着眼于多种眼底疾病的智能性、全面性诊断，对复杂性疾病进行综合性的辅助诊断；注重整合标准化、高质量的数据资源，提高算法性能、设计贴合临床的研究方案。

In recent years, artificial intelligence (AI) in ophthalmology has developed rapidly. Fundus image has become a research hotspot due to its easy access and rich biological information. The application of AI analysis in fundus image is under continuous development and exploration. At present, there have been many AI studies on clinical screening, diagnosis and prediction of common fundus diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma, and related achievements have been gradually applied in clinical practice. In addition to ophthalmic diseases, exploring the relationship between fundus features and various diseases and developing AI diagnostic systems based on this has become another popular research field. The application of AI in fundus image analysis will improve the shortage of medical resources and low diagnostic efficiency, and open up a “new track” for screening and diagnosis of various diseases. In the future, research on AI analysis of fundus image should focus on the intelligent and comprehensive diagnosis of multiple fundus diseases, and comprehensive auxiliary diagnosis of complex diseases, and lays emphasis on the integration of standardized and high-quality data resources, improve algorithm performance, and design clinically appropriate research program.

目的：分析全身麻醉在青光眼日间手术中的应用特点。方法：回顾性收集2023年1月—6月于中山大学中山眼科中心在全身麻醉下接受青光眼日间手术患者71例(全麻组)，并选择同时期在局部麻醉下接受青光眼手术患者154例(局麻组)，同时以年龄为配对因素行倾向评分匹配分析。主要观察指标为患者术后离院时间，并对患者的手术时间、手术前后眼压、视力、中央前房深度、周边前房深度、甘露醇使用情况以及术后较术前眼压降低差值进行分析。结果：与局麻组比较，全麻组患者术后离院时间未有延长(P>0.05)；全麻组患者术前眼压较高(P<0.001)、中央前房深度浅(P=0.018)、周边前房深度浅(P<0.001)；且全麻组患者在术后较术前眼压降低差值更大(P=0.002)。组间术后中央前房深度、术后使用甘露醇者比例及术后视力提高者比例比较差异均无统计学意义(P>0.05)。结论：术前眼部条件较差的青光眼患者能在全身麻醉下完成日间手术，且不延长术后离院时间。

Objective: To analyze the application of general anesthesia in the ambulatory glaucoma surgery. Methods: This retrospective study collected 71 patients who underwent ambulatory glaucoma surgery with general anesthesia at Zhongshan Ophthalmic Center of Sun Yat-sen University from January 2023 to June 2023, and 154 patients who underwent similar glaucoma surgery with local anesthesia during the same period. Age differences between the two groups were balanced by propensity score-matched analysis. The main outcome measures were the patients' post-operative discharge time, and the secondary end points included the duration of surgery, the patients' preoperative and postoperative intraocular pressure (IOP), visual acuity status, central anterior chamber depth, peripheral anterior chamber depth, mannitol use, the difference in IOP reduction after surgery compared to preoperative. Results: After propensity score-matched analysis, as compared with group L, general anesthesia did not prolong the patients' post-operative discharge time(P>0.05).Patients in the group G displayed significant higher IOP (P<0.001), shallower central anterior chamber depth (P=0.018), and shallower peripheral anterior depth (P<0.001). The dramatic reductions in IOP after surgery were exhibited in group G as compared with group L(P=0.002). There were no statistically significant differences in postoperative central anterior chamber depth, incidences in postoperative mannitol use, as well as incidences in visual acuity improvement on the first day after surgery. Conclusion: Glaucoma patients with poor preoperative ocular conditions were able to complete the complex glaucoma surgery under general anesthesia without prolonging their post-operative discharge time.

目的：评估新一代基于人工智能(artificial intelligence,AI)的人工晶状体(intraocular lens,IOL)计算公式的准确性。方法：本研究为回顾性研究，纳入因白内障行晶状体超声乳化联合IOL植入术的262例患者262眼。在术前，通过IOLMaster700获取角膜曲率、角膜白到白、中央角膜厚度、前房深度、晶状体厚度以及眼轴长度。使用第三代公式(SRK/T、Holladay 1和Hoffer Q)、Barrett UniversalⅡ(BUⅡ)、新一代AI公式(Kane、Pearl-DGS、Hill-RBF 3.0、Hoffer QST和Jin-AI)对术后屈光状态进行计算，并与术后实际的屈光状态进行比较。在将预测误差(prediction error,PE)归零后，分析了各公式的标准差(standard deviation,SD)、绝对误差均值(mean absolute error,MAE)、绝对误差中位数(median absolute error,MedAE)以及PE在±0.25、±0.50、±1.00、±2.00 D范围内的百分比。结果：基于AI的IOL屈光力计算公式的SD、MAE和MedAE的范围分别为0.37 D(Kane和Jin-AI)至0.39 D(Hoffer QST)、0.28 D(Hill-RBF 3.0和Jin-AI)至0.31 D(Hoffer QST)以及0.21 D(Hill-RBF3.0和Jin-AI)至0.24 D(HofferQST)；均低于第三代公式(SD：0.43 D~0.45 D；MAE：0.34 D；MedAE：0.25 D~0.28 D)。在所有公式中，Jin-AI公式预测误差在±0.50 D的比例最高，为84.73%，Kane(84.35%)和BUⅡ(83.97%)公式次之。结论：在IOL屈光力预测上，与传统第三代公式相比，新一代基于AI的公式表现出更高的准确性，可以使更多的患者在术后获得预期的屈光状态。

Objective: To evaluate the accuracy of new generation artificial intelligence (AI)-based intraocular lens (IOL)power calculation formulas. Methods: This retrospective study included a total of 262 eyes from 262 patients with cataract who underwent uneventful phacoemulsification combined with IOL implantation. Keratometry, corneal white-to-white, central corneal thickness, anterior chamber depth, lens thickness, and axial length were measured by the IOL Master 700 before surgery. Predicted refractive errors were calculated by the third-generation formulas (SRK/T, Holladay 1, and Hoffer Q), Barrett UniversalⅡ (BUⅡ), and the newer-generation AI formulas (Kane, Pearl-DGS, Hill-RBF 3.0, Hoffer QST, and Jin-AI), and were compared with the actual postoperative refractive value. After adjusting the prediction error (PE) to zero, the standard deviation (SD), mean absolute error (MAE), median absolute error (MedAE), and the percentage of a PE within the range of ±0.25 diopter (D), ±0.50 D, ±1.00 D, and ±2.00 D were analyzed. Results: The SD, MAE, and MedAE of the AI-based formulas ranged from 0.37 D (Kane and Jin-AI) to 0.39 D (Hoffer QST), 0.28 D (Hill-RBF 3.0 and Jin-AI) to 0.31 D (Hoffer QST), and 0.21 D (Hill-RBF 3.0 and Jin-AI) to 0.24 D (Hoffer QST), respectively. These values were all lower than those of the third-generation formula (SD: 0.43 D to 0.45 D; MAE: 0.34 D; MedAE: 0.25 D to 0.28 D). Among all the formulas, the Jin-AI formula had the highest proportion of a PE within ±0.50 D (84.73%), followed by Kane (84.35%) and BUⅡ (83.97%) formulas. Conclusion: The new AI-based IOL formulas show higher accuracy compared with the traditional third-generation ones in predicting IOL power. thereby enabling more patients to achieve the expected refractive outcomes after surgery

当前，药物临床试验面临着两大难题：数据真实性及相关人员操作规范性。现阶段国内外在药物临床试验方面的监管主要以事后监查为主，在数据质量管理以及操作规划标准的监查方面存在一定的时延性。而区块链通过非对称加密、哈希算法及智能合约等技术，可以在保证受试者隐私信息的前提下，提高政府相关监督机构的监管效率，提升药物临床试验数据管理的透明度；同时，与物联网的紧密结合可以实现对标准操作规范的进一步核查，与人工智能的结合有望实现受试者的自动招募。

Clinical drug trials are confronted with two major issues: first, data authenticity, for instance, if any data falsification is conducted during the whole trial; second, whether the standard of procedure is accordingly conducted throughout the whole trial or not. Currently, both domestic and overseas clinical drug trials are not supervised without delay (ex-post inspection). Blockchain technology can improve the efficiency of Food and Drug Administration and the transparency of trials while the rights and safety of human research subjects are guaranteed by the integrated technology such as chained structure, asymmetry key algorithm, hash algorithm, and smart contract. Furthermore, with the assistance of internet of things (IoT) and artificial intelligence (AI), the actual supervision over the whole trial and automatic recruitment of human research subjects are expected to achieve.

传统的眼底手术要求眼科医生具备精细的操作技术，但即便拥有再精湛的操作技术，眼底手术还是存在很大的风险性。因此，为了减少手术风险，提高手术质量，对传统眼底手术进行改进是十分必要的。近年来，在我国对于人工智能产业的大力支持之下，应用于各类行业的机器人随之诞生。机器人辅助系统(robot auxiliary system，RAS)在医学领域，特别是眼科学中应用广泛。对近几年RAS应用于眼底手术的案例进行整理总结，并将RAS参与的眼底手术以及传统的眼底手术进行对比，可以发现RAS在眼底手术中的应用可以显著提高手术效率，并降低手术风险。未来RAS的发展趋势可能着重聚焦于与深度学习算法的紧密结合。通过算法对手术中的视野图像进行预测、优化，从而让高精度的眼底手术更加高效、安全。

Traditional fundus surgery requires ophthalmologists to be equipped with sophisticated operating techniques, but even with the most sophisticated operating techniques, fundus surgery still has great risks. Therefore, in order to reduce the risk of surgery and improve the quality of surgery, it is very necessary to improve the traditional fundus surgery. In recent years, with China’s strong support for the artificial intelligence industry, robots used in various industries have been born. Robot auxiliary system (RAS) is widely used in the medical field, especially in ophthalmology. By summarizing the cases of fundus surgery with RAS in recent years and comparing the fundus surgery involving RAS with traditional fundus surgery, it can be found that the application of RAS in fundus surgery can significantly improve the efficiency of surgery and reduce the risk of surgery. The future development trend of RAS may focus on the close integration with deep learning algorithms, which can predict and optimize the field of view images during surgery so that high-precision fundus surgery can be more efficient and safer.

目的：探究可弯曲喉罩在经鼻内镜眼科日间手术中应用的安全性。方法：收集2023年1月—2024年1月在全身麻醉(全麻)下行经鼻内镜眼科日间手术成年患者资料，分析可弯曲喉罩使用的成功率，观察患者入室时、插管后、手术开始时、手术结束时的血流动力学变化情况；并记录喉罩置入后、手术开始5 min、手术开始30 min和手术结束时的呼吸参数及患者在麻醉恢复室（post-anesthesia care unit，PACU的停留期间的并发症。结果：共635例患者纳入分析，其中619例患者选用喉罩，607例患者喉罩置入成功，成功率98.1%，喉罩置入失败12例，均为诱导期间置入失败，占1.9%，无术中发生喉罩通气失败改气管插管病例。喉罩通气能满足经鼻内镜手术需求，表现为各时点通气量无显著变化及平稳的心率和平均动脉压。患者均顺利复苏，PACU停留时间为(34.6±11.2) min，喉罩拔除时间为(14.3±4.3) min，无二次气管内插管事件发生。结论：置入成功的可弯曲喉罩可安全应用在经鼻内镜眼科日间全麻手术中。

Objective: To investigate the safety of flexible laryngeal masks in the application of endoscopic ophthalmic ambulatory surgery. Methods: Data of adult patients undergoing endoscopic day ophthalmic surgery under general anesthesia from January 2023 to January 2024 were collected; the changes in hemodynamics at T1 (entry into the operating room), T2 (after intubation), T3 (before surgery), and T6 (after surgery) time points and the respiratory parameters at T2 (after intubation), T4 (5 minutes after surgery), T5 (30 minutes after surgery), and T6 (after surgery) time points were analyzed; meanwhile, the complications during the PACU stay were also analyzed. Results: A total of 635 patients were included in the retrospective analysis, of whom 619 patients were initially used FLMA, FLMA were successfully inserted in 607 patients with a rate of 98.1% while 12 patients suffered insertion failure during the period of anesthesia induction. Importantly, none of patients displayed FLMA ventilation failure and required tracheal intubation during the period of surgery. General anesthesia via FLMA ventilation can provide high quality of surgical field for endoscopic ophthalmic surgery, as evidenced by stable heart rates and mean arterial pressure and no significant change in minute ventilation at each time points. All patients recovered uneventfully with a PACU stay time of 34.6±11.2 min and FLMA removal time of (14.3±4.3) min. None of patients exhibited subsequently endotracheal intubation after FLMA removal. Conclusion: FLMA with successful placement can be safely used for endoscopic ophthalmic ambulatory surgery under general anesthesia.

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.