背景：目前已有研究报道了一种MATLAB的定制算法，用于Triton光学相干断层扫描血管成像(optical coherence tomography angiography，OCTA)图像的中心凹无血管区(fovea avascular zone，FAZ)的自动测量。由于这种算法非开源，且难以获取，因而大大限制了其在临床实践和科学研究中的应用。本研究提出一种用于Triton OCTA图像的FAZ自动分割的开源算法，即Smooth Level Sets macro(SLSM)算法，并将其测量结果与MATL AB和人工方法相比较，评估该算法分割的准确性和可靠性。方法：纳入35位健康受试者的35只健眼，选用Triton OCTA机器中的3 mm×3 mm扫描模式，对其黄斑区进行连续4次扫描。分别用人工和自动方法(包括MATL AB和SLSM)，测量浅层毛细血管图像中FAZ的面积、周长和圆度。分析各种自动算法的准确性、重复性，以及与人工方法结果的一致性。结果：SLSM算法的准确性仅低于人工方法，而高于MATLAB算法(Dice系数：人工方法，0.9568；SLSM，0.9506；MATL AB，0.9483)。SLSM和MATL AB测量FAZ面积的重复性均很高[组内相关系数(intraclass correlation coefficient，ICC)：SLSM，0.987；MATLAB，0.983]。SLSM、MATLAB测量FAZ面积的结果均与人工方法呈很高的一致性(ICC：SLSM，0.973；MATL AB，0.968)。结论：SLSM在Triton OCTA图像的FAZ自动分割中的准确性高于MATL AB，其测量结果与人工测量结果很相近。作为免费和开源的资源，SLSM有望成为Triton OCTA图像中有效可靠的FAZ自动分割和测量方法。

Background: Previous studies have proposed an automated customized program named MATLAB used in the foveal avascular zone (FAZ) measurements in Triton optical coherence tomography angiography (OCTA) images. But it is not open-source and not easy to obtain, which will largely restrict its application in clinical practice and medical research. In this study, we aimed to investigate the feasibility of the Smooth Level Sets macro (SLSM), a free and open-source program, and compared with the manual measurements and MATLAB in the FAZ quantification in Triton OCTA. Methods: Thirty-five eyes of 35 healthy subjects were scanned four times continuously using Triton OCTA. Manual and automated methods including the SLSM and MATLAB were used in the FAZ metrics (area, perimeter, and circularity) of the superficial capillary plexus. The accuracy, repeatability of all methods, and agreement between automated and manual methods were analyzed. Results: The SLSM presented higher accuracy with a higher average Dice coefficient (0.9506) than MATLAB (0.9483), which was just second to the manual method (0.9568). Both the SLSM [intraclass correlation coefficient (ICC) =0.987; coefficient of variation (CoV) =3.935%] and MATLAB (ICC =0.983; CoV =4.165%) showed excellent repeatability for the FAZ area. They also had excellent agreement with manual measurement (SLSM, ICC =0.973; MATLAB, ICC =0.968). Conclusion: The SLSM exhibits better accuracy than MATLAB in the automated FAZ measurement in Triton OCTA, the results of which were comparable to those obtained by manual measurement. This free and open-source program may be an accessible and feasible option for automated FAZ segmentation on Triton OCTA images.

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.

视神经属于中枢神经的一部分，损伤后难以再生。视神经损伤通常伴随视网膜神经节细胞(retinal ganglion cells，RGCs)的持续性凋亡及视神经变性坏死，引起视力损害甚至完全失明。目前针对视神经再生的基础研究主要集中于保护和维持视神经损伤后RGCs的存活、促进RGCs轴突再生及重建视神经功能。本文以RGCs保护、轴突再生及视神经功能重建等为关键词，查询国内外最新视神经再生研究类文献，并分析整理，从抗氧化应激、提供外源性细胞因子、炎症刺激、抗胶质瘢痕、基因调控等方面阐述近年的视神经再生研究进展，以期对后续的基础研究开展及临床转化有所帮助。

Optic nerves are a part of the central nervous system, which is difficult to regenerate after injury. Optic nerve injury is usually accompanied by continuous apoptosis of retinal ganglion cells (RGCs) and degeneration or necrosis of optic nerves, resulting in visual impairment or even complete blindness. At present, the basic research on optic nerve regeneration mainly focuses on protecting and maintaining the survival of RGCs after optic nerve injury, promoting RGCs axon regeneration, and reconstructing optic nerve function. In this paper, RGCs protection,axon regeneration, and optic nerve function reconstruction are used as key words to collect the latest domestic and foreign literatures on optic nerve regeneration. The research progress of optic nerve regeneration in recent years was reviewed from the aspects of antioxidant stress, provision of exogenous cytokines, inflammatory stimulation, anti-glial scar, gene regulation and so on, in order to help the follow-up basic research and clinical translation.

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.

目的：利用生物信息学方法分析与葡萄膜恶性黑色素瘤转移相关的非编码RNA，以及它们作为竞争性内源RNA的作用机制。方法：从癌症基因组图谱(The Cancer Genome Atlas，TCGA)数据库下载80例葡萄膜恶性黑色素瘤患者的RNA测序数据和临床资料，采用edgeR算法分析转移与非转移患者组织中差异表达(differentially expressed，DE)的长链非编码RNA(lncRNA)、微小RNA(miR)和mRNA，并构建lncRNA-miR-mRNA的竞争性内源RNA(competing endogenous RNA，ceRNA)调控网络，基因富集分析和通路分析研究网络中mRNA的生物学功能。Kaplan-Meier生存曲线分析ceRNA网络中核心RNA与生存率的关系。结果：从发生远处转移的葡萄膜恶性黑色素瘤样本中，共鉴定出346个上调的mRNA，118个下调的miR和45个上调的lncRNA。其中67个mRNA，7个miR和30个lncRNA相互组合形成616个ceRNA单元，并形成了一个具有181条边线ceRNA网络。基因富集分析表明：网络中的mRNA富集在肿瘤生成和转移相关的几个基因本体(Gene Ontology)和信号通路。拓扑分析确定了6个核心lncRNA(LINC00861、LINC02421、BHLHE40-AS1、LINC01252、LINC00513和LINC02389)和3个核心mRNA(UNC5D、BCL11B和MTDH)。 所有核心lncRNA、核心mRNA的表达水平和5个miR(miR-221、miR-222、miR-506、miR-507、miR-876)的表达水平均与总体生存率显着相关(均P<0.05)。结论：本研究揭示了几种lncRNA及其相关的ceRNA网络在葡萄膜恶性黑色素瘤转移中的作用，为进一步研究葡萄膜恶性黑色素瘤的发生和/或转移提供了新的方向。

Objective: To elucidate the expression of long non-coding RNAs (lncRNAs) and their roles as competing endogenous RNAs (ceRNAs) in uveal melanoma (UM) metastasis. Methods: RNA sequencing data and clinical information of 80 patients with UM were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed (DE) mRNAs, microRNAs (miR), and lncRNAs between metastatic and non-metastatic individuals with UM were screened using the edgeR algorithm. Gene enrichment analysis was conducted for the DE mRNAs. LncRNA-miR-mRNA regulatory triples and a ceRNA network were constructed. Betweenness centrality was used to screen hub genes and lncRNAs for subnetwork analysis. Kaplan-Meier survival analysis was conducted to explore correlations between the expression of hub RNAs and overall survival in the TCGA UM cohort. Results: A total of 346 upregulated mRNAs, 118 downregulated miRs, and 45 upregulated lncRNAs were identified in samples with systemic metastasis. Among them, 67 mRNAs, 7 miRs, and 30 lncRNAs mapped to 616 ceRNA triples, thus forming an interconnected ceRNA network with 181 edges. Gene enrichment analysis revealed that mRNAs in the network were enriched in multiple gene ontology terms and pathways associated with carcinogenesis and metastasis. Topological analysis identified 6 hub lncRNAs (LINC00861, LINC02421, BHLHE40-AS1, LINC01252, LINC00513, and LINC02389) and 3 hub mRNAs (UNC5D, BCL11B, and MTDH). The expression levels of all hub genes and 5 DEmiRs (miR-221, miR-222, miR-506, miR-507, miR-876) were significantly associated with the overall survival probability. Conclusion: This bioinformatic study revealed the functions of several lncRNAs and their associated ceRNA network in UM metastasis. It provides a novel in silicon evidence for future experimental study on the pathogenesis of systemic metastasis in uveal melanoma, especially from the perspective of non-coding RNA.

目的：探究囊袋张力环(CTR)植入对五种新一代人工晶状体(IOL)计算公式[Barrett Universal Ⅱ (BUⅡ), Emmetropia Verifying Optical (EVO), Kane, Pearl-DGS和Hill-RBF 2.0]在高度近视患者中预测准确性的影响。方法：前瞻性病例对照研究。观察2020年12月—2021年9月于陕西省眼科医院就诊的眼轴长度(axial length,AL)≥ 27.00 mm行白内障联合IOL(AR40E, 美国强生)植入术的患者。术眼随机分为植入CTR组(A组)和未植入CTR组(B组)。术前根据IOLMaster700测量眼部参数，使用BU Ⅱ公式计算所需IOL度数。记录术后1周、1个月及3个月实际等效球镜度(spherical equivalent,SE)，计算并比较五种公式预测误差(prediction error,PE)和绝对屈光预测误差(absolute Error,AE)。将A组和B组分别分为A1组(27.00 mm ≤ AL ≤ 30.00 mm)和A2组(AL>30.00 mm)；B1组(27.00 mm ≤ AL ≤ 30.00 mm)和B2组(AL >30.00 mm)，分析不同AL范围内CTR植入对公式预测准确性的影响。结果：共纳入患者63例(89眼)，年龄(55.93±10.17)岁，术前AL为(30.30±2.18)mm。A组、A1组及A2组术后不同时间SE值比较差异均无统计学意义(P>0.05)，B组、B1组及B2组术后1周与1个月，术后1周与3月SE值分别比较差异有统计学意义(P<0.05)，术后1个月与3个月比较，差异无统计学意义(P>0.05)。A组、B组、A1组、A2组、B1组和B2组各组中五种公式的AE值比较差异均无统计学意义(均P>0.05)。植入CTR后五种公式的预测误差变化比较差异无统计学意义(P>0.05)。结论：对于AL ≥27.00 mm的白内障患者，植入CTR组术后1周屈光度趋于稳定，未植入组术后1个月屈光度趋于稳定。CTR植入对五种公式预测准确性和选择无影响，五种计算公式均可正常选择。

Objective: To investigate the predictive accuracy and effect of capsular tension ring (CTR) implantation with five new generation intraocular lens (IOL) calculation formulas [Barrett Universal Ⅱ (BU Ⅱ), Emmetropia Verifying Optical(EVO), Kane, Pearl-DGS and Hill-RBF 2.0] in high myopia patients. Methods: This is a prospective case-control study. The patients were enrolled with an axial length (AL)≥27.00 mm, and underwent cataract surgery with AR40E IOL implantation at the Shaanxi Eye Hospital from December 2020 to September 2021. The patients were randomly assigned to the CTR implantation group (group A) and the non-CTR implantation group (group B). With the ocular parameters measured by the IOLMaster700, the IOL power was calculated with the BUⅡformula before surgery. The postoperative actual equivalent spherical diopter (SE) were recorded,and the predicted error (PE) and absolute error (AE) using the five formulas were recorded and compared at 1 week, 1 month, and 3 months, repsectively. Group A was divided to A1 (27.00 mm ≤ AL ≤ 30.00 mm) and A2 (AL>30.00 mm), and group B was divided to B1 (27.00 mm ≤ AL ≤ 30.00 mm) and B2 (AL>30.00 mm). The effects of CTR implantation and the accuracy of the formulas were analyzed with different AL ranges. Results: A total of 63 patients (89 eyes) were included, aged (55.93±10.17) years old, with preoperative AL (30.30± 2.18)mm. There was no statistically significant difference in SE between groups A, A1, and A2 (P>0.05) at different postoperative times. While there was a statistically significant difference in SE between groups B, B1, and B2 (P < 0.05) at 1 week and 1 month after surgery, and between 1 week and 3 months after surgery. There was no statistically significant difference between 1 month and 3 months after suergery (P>0.05). There was no significant difference in the AE using the five formulas among groups A, B, A1, A2, B1, and B2 (P>0.05). There was no statistically significant difference in prediction error changes among the five formulas after CTR implantation (P>0.05). Conclusion: For cataract patients with AL ≥ 27.00 mm, the refractionvalue in the CTR implantation group tended to stabilizeafter one week of surgery. While in the non-CTR implantation group, the refractionvalue tended to stabilize after one month. CTR implantation had no effect on the accuracy and selection of the five formula, and the five IOL calculation formulas can be normally selected.

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：比较不同预设光学区组的飞秒激光小切口基质透镜取出术(small incision lenticule extraction，SMILE)和飞秒激光制瓣联合准分子激光原位角膜磨镶术(femtosecond laser-assisted in situ keratomileuses，FS-LASIK)术后有效光学区(effective optical zone，EOZ)大小与角膜高阶像差(high order aberrations，HOAs)变化以及有效光学区大小对角膜高阶像差的影响。方法：收集2019年2月 至2020年5月来佛山市第二人民医院行激光手术的患者80例，均取右眼入组，SMILE组43例，FSLASIK组37例，按预设光学区大小分为6.5 mm和6.0 mm组，分别于术前和术后1个月应用Pentacam三维眼前节分析系统测量有效光学区及总角膜高阶像差、彗差、球差，分析不同预设光学区下SMILE组和FS-LASIK组有效光学大小及与角膜高阶像差的关系。结果：在相同预设光学区下，术后1个月SMILE组的有效光学区均大于FS-LASIK组，差异均有统计学意义(均P<0.05)，且SMILE组的角膜总高阶像差、球差、彗差均较FS-LASIK组低，差异均有统计学意义(均P<0.05)；对于相同手术方式，预设光学区6.0 mm较6.5 mm组，术后1个月角膜总高阶像差、球差、彗差均升高，差异均有统计学意义(均P<0.05)。结论：SMILE组和FS-LASIK组术后1个月的有效光学区均小于预设光学区，SMILE组大于FS-LASIK组；术后1个月有效光学区越大，角膜高阶像差越小；在相同预设光学区下，SMILE组术后1个月角膜高阶像差小于FS-LASIK组。

Objective: To compare the effective optical zone (EOZ) and the changes in corneal high order aberrations (HOAs) after small incision lenticule extraction (SMILE) with those after femtosecond laser-assisted in situ keratomileuses (FS-LASIK). Methods: This study included 80 subjects who underwent laser refractive surgery at the Second People’s Hospital of Foshan between February 2019 and May 2020. Only data from the right eye of each subject were analyzed. A total of 43 eyes underwent SMILE while 37 eyes received FS-LASIK. The eyes were further stratified into subgroups based on different programmed optical zones: the 6.5 mm group and the 6.0 mm group. EOZ, coma, and spherical aberration were measured with Pentacam 3D anterior segment analysis system preoperatively and one month postoperatively. In addition, the relationship between EOZ and corneal HOAs was analyzed and compared between different optical-zone groups after SMILE and FS-LASIK. Results: For the same programmed optical zone, the SMILE group achieved a significantly greater EOZ than the FS-LASIK group who was measured 1-month postoperatively did (P<0.05). Meanwhile, corneal HOAs, spherical aberration, and coma in the SMILE group are significantly lower than those in the FS-LASIK group (P<0.05). For the same procedure (SMILE or FS-LASIK), the 6.0 mm group demonstrated significantly higher corneal total HOAs, spherical aberration, and coma than the 6.5 mm group did 1-month after the surgery (P<0.05). Conclusion: In both the SMILE and the FS-LASIK groups, 1-month postoperative EOZ was smaller than the programmed optical zone. EOZ in the SMILE group was larger than that in the FS-LASIK group. The larger the 1-month postoperative EOZ was, the lower corneal HOAs were. For the same programmed optical zone, 1-month postoperative corneal HOAs in the SMILE group is lower than that in the FS-LASIK group.

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

视网膜是中枢神经系统的一部分。在胚胎起源上，视网膜和大脑均由神经管发育而来。因此，许多发生在大脑的神经退行性疾病往往会同时累及视网膜。而神经退行性疾病过程中相关的特征性病理改变，如病理性蛋白聚集和神经血管单元破坏也常能在视网膜组织中被检测到。在一些神经退行性疾病中，眼部的病理改变甚至在临床症状出现之前就已发生；其次视网膜易于观察且局部治疗操作便捷，因此近年来视网膜在中枢神经退行性疾病发病机制研究、早期诊断和新型治疗方式探究等方面备受关注。该文对常见神经退行性疾病的眼部病理改变进行综述，旨在为大脑和视网膜神经退行疾病的发病机制、诊断以及治疗研究提供新的见解。

The retina is a part of the central nervous system. Developmentally, both retina and brain are derived from the neural tube. Therefore, many neurodegenerative diseases that occur in the brain tend to involve both the retina. In the process of neurodegenerative diseases, related characteristic pathological changes, such as pathological protein aggregation, neurovascular unit impairment can often be detected in retinal tissue. In some neurodegenerative diseases, pathological changes in the eye occur even before clinical symptoms appear. In addition, the retina are easy to observe and local treatments are convenient. In recent years, the manifestations of the retina have attracted much attention in the study of pathogenesis, early diagnosis, and new treatments of systemic central neurodegenerative diseases. In this way, this article reviews the ocular pathological changes of common neurodegenerative diseases, aiming to provide new insights into the pathogenesis, diagnosis, and treatment of brain and retinal neurodegenerative diseases.