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.

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.

Aims: To identify the characteristic retinal neurovascular changes in patients in different stages of nondiabetic chronic kidney disease (CKD) and to develop a model for the accurate diagnosis of nondiabetic CKD.
Methods: Peripapillary retinal nerve fiber layer (pRNFL) thickness and average macular ganglion cell-inner plexiform layer (GC-IPL) thickness of nondiabetic CKD patients and healthy controls (HC) were evaluated by spectral-domain optical coherence tomography (OCT). The vessel density (VD) and perfusion density (PD) of the macula were obtained from optical coherence tomography angiography (OCTA). The estimated glomerular filtration rate (eGFR) was obtained to access the kidney function of CKD patients. Multiple linear regression models were used to adjust for confounding factors in statistical analyzes. The diagnostic capabilities of the parameters were evaluated by logistic regression models.
Results: 131 nondiabetic CKD patients and 62 HC entered the study. eGFR was found significantly associated with parafoveal VD and PD (average PD: β = 0.000 4, P_adjusted < 0.001) in various sectors. Thinning of pRNFL (β = -6.725, P_adjusted < 0.001) and GC-IPL (β = -4.542, P_adjusted < 0.001), as well as decreased VD (β = -2.107, P_- adjusted < 0.001) and PD (β = -0.057, P_adjusted = 0.032 8) were found in CKD patients. Thinning of pRNFL and deteriorated perifoveal vasculature were found in early CKD, and the parafoveal and foveal VD significantly declined in advanced CKD. Logistic regression models were employed, and selected neurovascular parameters showed an AUC of 0.853 (95% Confidence Interval [CI]: 0.795 to 0.910) in distinguishing CKD patients from HC.
Conclusions: Distinctive retinal neurovascular characteristics could be observed in nondiabetic CKD patients of different severities. Our results suggest that retinal manifestations could be valuable in the screening, diagnosis, and follow-up evaluation of patients with CKD.

Aims: To identify the characteristic retinal neurovascular changes in patients in different stages of nondiabetic chronic kidney disease (CKD) and to develop a model for the accurate diagnosis of nondiabetic CKD.
Methods: Peripapillary retinal nerve fiber layer (pRNFL) thickness and average macular ganglion cell-inner plexiform layer (GC-IPL) thickness of nondiabetic CKD patients and healthy controls (HC) were evaluated by spectral-domain optical coherence tomography (OCT). The vessel density (VD) and perfusion density (PD) of the macula were obtained from optical coherence tomography angiography (OCTA). The estimated glomerular filtration rate (eGFR) was obtained to access the kidney function of CKD patients. Multiple linear regression models were used to adjust for confounding factors in statistical analyzes. The diagnostic capabilities of the parameters were evaluated by logistic regression models.
Results: 131 nondiabetic CKD patients and 62 HC entered the study. eGFR was found significantly associated with parafoveal VD and PD (average PD: β = 0.000 4, P_adjusted < 0.001) in various sectors. Thinning of pRNFL (β = -6.725, P_adjusted < 0.001) and GC-IPL (β = -4.542, P_adjusted < 0.001), as well as decreased VD (β = -2.107, P_adjusted < 0.001) and PD (β = -0.057, P_adjusted = 0.032 8) were found in CKD patients. Thinning of pRNFL and deteriorated perifoveal vasculature were found in early CKD, and the parafoveal and foveal VD significantly declined in advanced CKD. Logistic regression models were employed, and selected neurovascular parameters showed an AUC of 0.853 (95% Confidence Interval [CI]: 0.795 to 0.910) in distinguishing CKD patients from HC.
Conclusions: Distinctive retinal neurovascular characteristics could be observed in nondiabetic CKD patients of different severities. Our results suggest that retinal manifestations could be valuable in the screening, diagnosis, and follow-up evaluation of patients with CKD.

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.

人工智能是对人类智能的模拟和拓展。基于深度学习的人工智能可以很好地利用图像的内在特征，如轮廓、框架等，来分析图像。研究人员通常利用图像来诊断眼底病，因此将人工智能应用于眼底检查是有意义的。在眼科领域，人工智能通过分析光学相干断层扫描图像、眼底照片和超宽视野图像，已经在检测多种眼底疾病上取得了类似医生的性能。它也已经被广泛应用于疾病进展预测。然而，人工智能在眼科的应用也存在一些潜在的挑战，黑盒问题是其中之一。研究人员致力于开发更多的可解释的深度学习系统，并确认其临床可行性。人工智能在最流行的眼底病中的最新应用、可能遇到的挑战以及未来的道路将一一阐述。

Artificial intelligence (AI) is about simulating and expanding human intelligence. AI based on deep learning (DL) can analyze images well by using their inherent features, such as outlines, frames and so on. As researchers generally diagnoses ocular fundus diseases by images, it makes sense to apply AI to fundus examination. In ophthalmology, AI has achieved doctor-like performance in detecting multiple ocular fundus diseases through optical coherence tomography (OCT) images, fundus photographs, and ultra-wide-field (UWF) images. It has also been widely used in disease progression prediction. Nonetheless, there are also some potential challenges with AI application in ophthalmology, one of which is the black-box problem. Researchers are devoted to developing more interpretable deep learning systems (DLS) and confirming their clinical feasibility. This review describes a summary of the state-of-the-art AI application in the most popular ocular fundus diseases, potential challenges and the path forward.

Age stands as a primary risk factor for diseases and disabilities among the elderly. To effectively assess the underlying aging processes, accurate measures of biological age and rates of aging across multiple levels of aging features are essential. Biological age derives from physiological assessments of systems and organs. It has emerged as a superior predictor of age-related diseases and mortality compared to chronological age. Recent advancements in machine learning have catalyzed the development of sophisticated models capable of quantitatively characterizing biological aging with different types of data. This review explores the machine learning models in advancing our understanding of biological aging, highlighting the potential of these innovative approaches to facilitate aging research and personalized healthcare strategies.

Age stands as a primary risk factor for diseases and disabilities among the elderly. To effectively assess the underlying aging processes, accurate measures of biological age and rates of aging across multiple levels of aging features are essential. Biological age derives from physiological assessments of systems and organs. It has emerged as a superior predictor of age-related diseases and mortality compared to chronological age. Recent advancements in machine learning have catalyzed the development of sophisticated models capable of quantitatively characterizing biological aging with different types of data. This review explores the machine learning models in advancing our understanding of biological aging, highlighting the potential of these innovative approaches to facilitate aging research and personalized healthcare strategies.

近年来随着人口老龄化的发展、人群用眼方式的改变，现有的眼科医疗资源正越来越难以满足日渐增长的医疗需求，亟需新型的诊疗模式予以补足。眼科人工智能作为眼科领域的新兴元素，在眼病的筛查诊断中发展迅速，主要表现为“眼部图像数据+人工智能”的模式。近年来，随着该模式在白内障、青光眼、糖尿病性视网膜病变(diabetic retinopathy，DR)等常见病中研究的深入，相关技术日渐成熟，表现出了较大的应用优势与应用前景，部分技术甚至成功转化并被逐渐应用于临床。眼科诊疗向智慧医学模式的过渡，有望缓解日益增长的医疗需求与紧缺的医疗资源之间的矛盾，从而提高整体的医疗服务水平。

The development of population aging and changes in the way people use their eyes over the recent years have increasingly challenged the existing ophthalmic medical resources to meet the growing medical needs, thus urgently calling for a novel diagnostic and treatment mode. Despite its status as an emerging sector in ophthalmology, ophthalmic artificial intelligence has developed rapidly in the screening and diagnosis of eye diseases, as can be seen in practices adopting the “eye imaging data + AI” mode. In recent years, with the intensified research on this mode with respect to common diseases such as cataract, glaucoma and diabetic retinopathy, relevant technologies have grown increasingly mature, presenting undeniable application superiority and prospects. Some of the relevant technical achievements have also been successfully transformed for practical usage, and are gradually being applied to clinical practices. Ophthalmic diagnosis and treatment are transitioning toward the era of intelligent medical services, which are expected to reduce the contradictions between the growing medical needs and the shortage of medical resources, as well as ultimately improve the overall experience of medical services.