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山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (11): 24-32, 38.doi: 10.6040/j.issn.1671-7554.0.2020.1249

• 眼科人工智能新进展专题 • 上一篇    下一篇

青光眼影像人工智能深度学习研究现状与展望

CheungCarol Y.*(),冉安然   

  1. 香港中文大学眼科与视觉科学系，香港 999077
  • 收稿日期:2020-09-04 出版日期:2020-11-10 发布日期:2020-11-04
  • 通讯作者: CheungCarol Y. E-mail:carolcheung@cuhk.edu.hk
  • 作者简介:Carol Y. Cheung,香港中文大学眼科及视觉科学系助理教授，博士研究生导师。主要研究领域是“眼部成像”，其理论基础为眼睛是人体循环系统和神经系统的“窗口”。在香港和新加坡有10多年从事眼部成像研究的经验，致力于开发和应用图像分析以及人工智能技术对糖尿病性视网膜病变、青光眼和阿尔茨海默病的研究|目前从事多项研究，包括开发和应用新型成像技术，以及探索如何利用这些技术改善临床工作流程和公共卫生。为人们理解和认识主要眼病以及脑部疾病提供新的角度，促进用先进的眼部成像技术进行更有针对性、有效的疾病筛查，实现防盲和脑部疾病的早期检测。在国际索引的同行评审期刊发表200多篇有影响力的论文，并撰写10本书的相关章节，专注于与视网膜、脑部疾病有关的视网膜成像技术|国际科学期刊和国际资助基金会的定期审稿人，亚太眼科影像学会秘书长，亚太远程眼科学会理事会理事兼财务主管,海峡两岸医学与健康交流协会视网膜血管疾病委员会的名誉会员，以及中国医学教育协会智能医学专业委员会智能眼科学组的理事会成员

Artificial intelligence deep learning in glaucoma imaging: current progress and future prospect

Carol Y. Cheung*(),Anran RAN   

  1. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
  • Received:2020-09-04 Online:2020-11-10 Published:2020-11-04
  • Contact: Carol Y. Cheung E-mail:carolcheung@cuhk.edu.hk

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摘要：

青光眼是一组异质性神经退行性疾病，其特征是视网膜神经节细胞及其轴突逐渐消失，现已成为全球不可逆性失明的主要原因。人工智能(AI)是由机器展示的智能，而深度学习(DL)是其中一个基于深度神经网络的分支，在医学成像领域取得了重大突破。在青光眼影像方面，已有越来越多的研究将DL应用于眼底图像以及光学相干断层扫描(OCT)，以检测青光眼性视神经病变。有很好的结果显示，将DL技术整合到影像中进行青光眼评估是高效、准确的，这可能会解决当前实践和临床工作流程中的一些难题。但是，未来进一步的研究对于解决现存挑战至关重要，例如为不同研究之间的图像标记建立标准，将“黑匣子”的学习过程进行可视化，提高模型在未知数据集上的泛化能力，开发基于DL的实际应用程序，以及建立合理的临床工作流程，进行前瞻性验证和成本效益分析等。本文总结了AI应用于青光眼影像的最新研究现状，并讨论了对临床的潜在影响和未来的研究方向。

关键词: 青光眼影像, 人工智能, 光学相干断层扫描, 眼底照相, 深度学习

Abstract:

Glaucoma is a group of heterogeneous neurodegenerative diseases, characterized by the gradual loss of retinal ganglion cells and their axons, and has now become the major reason of irreversible blindness worldwide. Artificial intelligence (AI) is intelligence demonstrated by machines. Deep learning (DL) is a subset of AI based on deep neural networks, and it has made great breakthroughs in medical imaging. In glaucoma imaging, research interests have been increasing on applying DL in fundus photographs and optical coherence tomography (OCT) for glaucomatous optic neuropathy (GON) detection. Promising results show that the incorporation of DL technology in imaging for glaucoma assessment is efficient and accurate, which could potentially address some gaps in the current practice and clinical workflow. However, further research is crucial in tackling some existing challenges, such as setting a standard for ground truth labelling among different studies, visualizing the learning process in the "black box", improving the model generalizability on unseen datasets, developing the DL-powered infrastructure for real-world implementation, establishing a practical clinical workflow, conducting prospective validation and cost-effectiveness analysis. This review summarizes recent studies on the application of AI on glaucoma imaging, discusses the potential clinical impact and future research directions.

Key words: Glaucoma imaging, Artificial intelligence, Optical coherence tomography, Fundus photography, Deep learning

中图分类号: 

  • R574

图1

人工智能、机器学习以及深度学习"

表1

DL模型开发与性能评估的基本数据设置"

术语 说明
训练集 与调试集和内部验证集来自同一个大的数据集，是基于一定比例划分且互不重合，用于建立深度学习模型。研究者可构建具有不同参数甚至不同网络架构的DL模型来学习训练集中青光眼性视神经改变的相关特征。
调试集 与训练集和内部验证集来自同一个大的数据集，是基于一定比例划分且互不重合，用于实时评估不同模型在训练期间的性能。研究者可根据实时观察的学习曲线进行参数调节，确定训练停止节点，并最终选择最佳DL模型。
内部测试集 与训练集和调试集来自同一个大的数据集，是基于一定比例划分且互不重合，用于评估所选的最佳DL模型在该未知数据集上的性能。临床研究中，研究者需汇报模型在内部测试集上的性能，通常包括受试者工作特征曲线下面积，灵敏度，特异性以及准确性等。
外部测试集 一个或多个独立的数据集，与训练集、测试集和内部验证集来自不同数据集，用于评估上述DL模型在其他未知数据集上的性能，验证其临床通用性。研究者同样需汇报模受试者工作特征曲线下面积、灵敏度、特异性以及准确性等。当DL模型在内部以及外部测试集都具有较好性能时，即证明该模型有良好的通用性。
K-倍交叉验证 主要用于数据量有限时。将整个大的数据集分为k个相等的小数据集，其中(k-1)个合并为训练集用于模型开发，剩下的一个作为测试集评估模型性能。该训练过程重复k次，每次选择其中一个小数据集用作测试集。研究者应汇报所有测试集的平均性能，通常为${\bar x}$±s。
留一法交叉验证 主要用于数据量非常有限时，是一种特殊类型的K-倍交叉验证，此时K等于数据总数(即标注图像的总数)。仅留下一张图像来测试模型性能，其他图像全部用于训练。该训练过程同样重复k次，研究者应汇报所有测试集的平均性能，通常为${\bar x}$±s。

图2

DL模型的开发与性能评估基础步骤"

图3

基于光学相干断层扫描(optical coherence tomography, OCT)不同类型输入的DL模型 A：传统OCT报告；B:视盘区未分割二维扫描图；C：视盘区立体扫描图。"

图4

人工智能青光眼影像分析系统在临床中辅助青光眼检测或筛查的工作流程"

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