| 人工智能及其在眼科疾病诊疗中的应用 |
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| 引用本文： | 孙铁,张雨晴,邵毅. 人工智能及其在眼科疾病诊疗中的应用[J]. 眼科新进展, 2020, 0(8): 793-796. DOI: 10.13389/j.cnki.rao.2020.0181 |
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| 作者姓名： | 孙铁 张雨晴 邵毅 |
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| 作者单位： | 330006 江西省南昌市，南昌大学第一临床附属医院 |
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| 摘 要： | 随着诊断技术的进步，人们对眼部的结构及其相关病变有了更为深入的了解:角膜共聚焦显微镜可提供角膜内不同层次的详细视图；而数字图像则能提供丰富的形态数据集。为了能够自动提取与眼科疾病相关的临床信息，在对正常角膜进行评估的同时识别异常的角膜，人们将人工智能（artificial intelligence，AI）与眼部结构联系起来。与传统的信息处理技术相比，AI具有更高的准确性，并能进行快速、无创的综合分析。基于神经网络的机器学习和深度学习方法能够识别、定位和量化大量眼科疾病中的病理特征，并作出推断或预测。AI的应用前景包括自动检测疾病的发生、筛选、诊断分级以及治疗指导，治疗效果的量化以及全新治疗方法的鉴定。预测和预后功能进一步扩展了AI在眼科中的应用潜力，这将实现医疗保健的个体化以及大规模管理，协助眼科医生提供高质量的诊断和治疗，并应对更复杂的临床难题。
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| 关 键 词： | 人工智能 眼科疾病 共聚焦显微镜 人工神经网络 图像分类与分析 |
The application of artificial intelligence on ophthalmic diseases |
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| SUN Tie,ZHANG Yuqing,SHAO Yi. The application of artificial intelligence on ophthalmic diseases[J]. Recent Advances in Ophthalmology, 2020, 0(8): 793-796. DOI: 10.13389/j.cnki.rao.2020.0181 |
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| Authors: | SUN Tie ZHANG Yuqing SHAO Yi |
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| Affiliation: | Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China |
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| Abstract: | With the development of diagnostic technology, people have a deeper understanding of the structure of the eye and its related lesions: corneal confocal microscopy can provide detailed views of different levels of cornea, while digital images can provide a rich set of morphological data. In order to automatically extract clinical information related to ophthalmic diseases, evaluate normal cornea and identify abnormal cornea, artificial intelligence (AI) is associated with eye structure. Compared with the traditional information processing technology, the artificial intelligence method has higher accuracy and can be used for rapid and non-invasive comprehensive analysis. Machine learning and in-depth learning based on neural networks can identify, locate and quantify pathological features in a large number of ophthalmological diseases to simulate the path of recognition of human brain objects. AI’s application prospects include automatic detection of disease occurrence, screening, diagnosis grade and treatment guidance, quantification of therapeutic effect and evaluation of new treatment methods. Predictive and prognostic conclusions further expand the application potential of AI in ophthalmology, which will enable personalized health care and large-scale management, assist ophthalmologists in providing high-quality diagnosis and treatment, and cope with more complex clinical problems. |
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| Keywords: | artificial intelligence ophthalmic diseases confocal microscopy artificial neural network image classification and analysis |
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