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摘要: 慢性阻塞性肺疾病(chronic obstructive pulmonary disease, COPD)是一种以中性粒细胞浸润所致炎症为特征的呼吸道疾病, 目前已成为全球第三大死亡原因。发生COPD后, 中性粒细胞持续聚集可促进中性粒细胞胞外陷阱(neutrophil extracellular traps, NETs)过度形成, 在局部捕获与清除病原体、快速控制感染、免疫调节等方面发挥重要作用。本文主要介绍COPD发生与NETs形成机制以及NETs在COPD中的作用研究进展, 并梳理基于NETs进行COPD治疗的相关药物靶点, 以期为进一步研究提供参考。Abstract: Chronic obstructive pulmonary disease (COPD), a respiratory disease characterized by inflammation due to neutrophil infiltration, has become the third leading cause of death worldwide. After the occurrence of COPD, the persistent accumulation of neutrophils can promote the excessive formation of neutrophil extracellular traps (NETs), which plays an important role in local capture and clearance of pathogens, rapid control of infection, and immune regulation. This article mainly introduces the mechanism of COPD occurrence and NETs formation as well as the research progress of NETs in COPD, and summarizes the relevant drug targets for COPD treatment based on NETs, aiming to provide a reference for further research.
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近年来，人工智能(artificial intelligence，AI) 技术正逐渐渗透到多个医学专科领域，推动临床诊疗发生了前所未有的变革，该技术的优势在于建模和优化，以人工神经网络、进化算法和模糊系统最为常用[1]。目前，AI技术在眼科领域的应用得到了迅猛发展，其诊断迅速、精确度高且客观可信，可优化眼科患者的诊疗模式，极大提升临床诊断效率。AI眼科研究主要集中于建立眼科检查图像处理数据库和提取使用医疗记录中的数据方面[2]，由于从标准化图像中可获取诸多疾病相关特征信息，因此前者的应用更为广泛，其中最常用的是基于深度学习(deep learning, DL)的卷积神经网络(convolutional neural network, CNN)算法，训练时特征提取和模式分类可同时进行和并行学习。目前，基于DL的相关眼科研究涉及眼表、青光眼、白内障等多个亚专科[3]，本文谨对AI技术辅助眼底疾病诊疗及技术转化过程中的制约因素进行分析，以期提高AI技术在眼底疾病中的辅助诊疗水平。
1. AI技术辅助眼底疾病诊疗
AI技术的迅速发展，特别是机器学习中的DL技术，为眼科临床诊疗开启了新模式。眼科辅助检查模式多样，具有较强的专业性，AI技术在眼科的应用，极大提高了眼科临床诊疗效率。目前，AI技术在眼底疾病的诊疗应用主要包括筛查、分级与分期、预测与监测等方面。
1.1 眼底疾病筛查
由于大部分眼底疾病早期症状相对轻微，且社区和体检机构的眼科专科医生相对缺乏，普通全身体检经常忽略眼底的检查，极易导致眼底疾病漏诊。当患者因严重视力下降到综合医院就诊时，疾病往往已进展至中后期，一方面增加了治疗难度，另一方面大大增加了医疗成本。例如糖尿病视网膜病变(diabetic retinopathy，DR)，如能在疾病早期及时诊断，予以干预和局部治疗，将避免进展成增殖性糖尿病视网膜病变(proliferative diabetic retinopathy，PDR)，以及新生血管青光眼甚至失明等严重并发症。
明帅等[4]通过比较社区医疗机构和医院利用彩色眼底图像的AI阅片系统诊断DR的差异，发现社区的DR阴性率和无需转诊比例均高于医院，而医院的AI诊断以需转诊DR为主，且已治疗DR的比例更高，这提示不同诊疗场景因疾病构成类型不同，对AI辅助诊断设备的要求也各异。在社区医疗机构中，若AI诊断早期眼底疾病出现假阴性，包括年龄相关性黄斑变性(age-related macular degeneration, AMD)、轻度非增殖性糖尿病视网膜病变(mild non-proliferative diabetic retinopathy, NPDR)等，产生的医疗支出和医疗风险相对较低；AI系统漏诊中重度NPDR或中度AMD，则会延误患者的最佳治疗时机，导致医疗成本增加。因此，就社区医疗机构而言，较高的灵敏度是眼底疾病筛查AI系统重要的评价指标，能够减少中重度眼底疾病的漏诊，但该系统目前仍缺乏明确的诊疗建议输出。由于社区医疗机构的医务人员对眼科专科疾病的了解较少，可能无法针对AI系统输出的眼底图像病灶特征或诊断标签提供合适的临床诊疗管理建议，如三级医院就诊、急诊处理、定期随访等。因此，能够同时输出眼底疾病筛查诊断与相关医疗建议的AI系统在社区医疗机构筛查场景中可能更为实用。
1.2 眼底疾病分级与分期
社区医疗机构和医院体检中心对AI系统的诉求主要是自动输出诊断结果及治疗建议，而三级医院的眼科专科中心主要使用AI系统进行辅助诊断，该系统的主要作用是协助专科医生识别和统计特异的眼底病灶，对常见眼底疾病提供分级参考，最终由医生结合全部临床评估作出诊断。同时，该系统还需识别不同模式的眼底图像，包括彩色眼底照相(color fundus picture, CFP)、眼底自发荧光照相(fundus autofluorescence，FAF)、光学相干断层成像(optical coher-ence tomography, OCT)、光学相干断层扫描血管成像(optical coherence tomography angiography，OCTA)、荧光素钠血管造影(fluorescein angiography，FA)和吲哚菁绿血管造影(indocyanine green angiography，ICGA)。眼底疾病的分级与分期常需结合不同影像学结果，如息肉状脉络膜视网膜病变(polypoidal choroidal vasculopathy，PCV)、DR和AMD的分级。例如笔者团队研究设计的CNN系统可同时识别CFP和OCT，在AMD的数据集中，进行干性AMD、新生血管性AMD，PCV和正常人的四分类诊断，准确率达87.4%，不亚于眼科专科医生[5]。Peng等[6]训练的DeepSeeNet系统对年龄相关眼病研究(age related eye disease studies，AREDS) 的严重度分级准确率及晚期AMD的检出率均不亚于专科医生；同时该系统对网状假玻璃膜疣和视网膜下玻璃膜疣样沉积病灶的识别可辅助提示晚期AMD的预后。因此，AI系统辅助眼科医生对病灶进行识别和疾病分期，能够显著提高临床诊疗效率。
1.3 眼底疾病预测与监测
许多常见眼底疾病(包括高血压性视网膜病变、DR、AMD等)呈慢性病程，需对患者的眼底进行定期随访与监测，AI系统主要应用于辅助眼底疾病患病风险预测、疗效预测和进展程度预测等方面。Burlina等[7]使用超过65 000张眼底彩照训练ResNet50 CNN模型，该模型对AMD的5年风险预测平均误差为3.5%~5.5%，准确率优于眼科医生。Yoo等[8]设计DeepPDT-Net模型，并通过CFP预测中心性浆液性脉络膜视网膜病变(central serous chorioretinopathy，CSC)患者接受光动力疗法(photody-namic therapy，PDT)的治疗效果，准确率达88%，可辅助眼底专科医生解决预测疗效这一棘手问题。此外，AI系统还可通过OCT图像实现视网膜静脉阻塞抗血管内皮生长因子(vascular endothelial growth factor，VEGF)治疗的疗效预测[9]和对干性AMD地图样萎缩患者的视力预测[10]等。
2. AI眼底研究技术转化
2019—2021年，医疗AI中的视网膜影像市场以171%的复合增长率一骑绝尘，较心血管影像(104.4%)和肺部影像(114.4%)市场增长更快。但目前国内批准上市的AI产品中视网膜筛查设备仍较少，如鹰瞳公司研发的Airdoc-AIFUNDUS，可适用于医疗机构和大健康场景的筛查设备，能够筛查DR、高血压视网膜病变、AMD、视网膜静脉阻塞、视网膜脱离和病理性近视等；硅基仿生、Vistel、Shang Gong和VoxelCloud公司也有经国家药品监督管理局注册的DR筛查视网膜影像识别AI产品。由于AI研究成果的产品转化受训练数据、研发能力、临床验证和市场适应性等多种因素影响，目前仅有少数AI研究成果可完全转化成产品进入市场，针对多病种的眼底筛查、疾病分期和治疗预后等的AI产品仍相对稀缺。
2.1 AI技术转化的影响因素
制约AI技术转化为产品的因素主要包括：(1)医学方面，AI产品需要大量的真实世界视网膜影像数据，并由医学专家进行标注和深度学习算法训练，从而达到不亚于临床专家的高准确率；(2) 工程学方面，深度学习算法开发和企业的研发能力是AI产品的核心，需多方专业和具有交叉学科经验的人才互相协调合作才能高效地实现产品成型和研发；(3)监管方面，AI产品在申请注册前还需充分的临床前研究和临床试验证明其有效性和安全性；(4)市场推广方面，AI产品需提高各类医疗场景中的适应性和充分的市场教育和推广，使工作人员和患者产生使用AI产品的意愿，这直接影响AI产品的利用率和市场潜力。
2.2 AI技术转化的其他途径
AI技术除能够辅助医生进行临床诊疗外，还可结合大语言模型，通过可穿戴设备及手机软件等途径，并结合患者个性化病程，自动提醒随访诊疗及监测患者疾病进展，实现对患者的随访管理。AI系统通常被喻为“黑箱”，其内部工作原理对用户不可见，人们可以向其提供输入并获得输出，但是不能检查产生输出的系统代码或逻辑，越来越多的研究人员聚焦于提升AI系统的可解释性，以发现影响疾病诊断的有意义的病理结构，从而帮助理解疾病的发展规律[11]。
3. 小结
AI医学是全球大力推动的医工结合交叉学科，针对AI眼底疾病诊疗的发展方向和技术成果转化，笔者提出了AI适用于不同医学和大健康应用场景的眼底筛查、眼底疾病分级和预测三种诊疗模式，分析了AI研究成果实现技术转化的重要因素，包括大量真实世界的医学专家标注金标准图像数据库、工程学交叉学科人才进行产品研发、严格的临床试验和充分的市场推广。未来期望AI眼底影像系统更广泛地应用于临床，以帮助更多患者实现早期筛查诊断，提高诊疗效率，降低医疗成本。
作者贡献：许金回负责文献检索、论文撰写；刘苗苗、李雅亭、武兴东、朱浩斌负责论文修订；岳红梅负责提供论文写作思路。利益冲突：所有作者均声明不存在利益冲突 -
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