标准化设计与应用指南:基于眼底彩色摄影的黄斑病变征象初级人工智能筛查系统

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Intelligent medicine Pub Date : 2023-08-01 DOI:10.1016/j.imed.2023.05.001
Ocular Fundus Diseases Group of Chinese Ophthalmological Society; Expert Group for Artificial Intelligence Research, Development, and Application
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引用次数: 1

摘要

随着人工智能的普及和发展,基于人工智能算法的疾病筛查系统正在医学领域逐渐兴起。这种系统可用于疾病的初级筛查,以减轻初级卫生保健的压力。近年来,人工智能算法在眼底彩色摄影黄斑区病变征象的分析和识别方面表现出了良好的性能,未来势必会出现一种适用于初筛的眼底病变征象筛查系统。因此,为了规范基于人工智能算法的黄斑区病变体征筛查系统的设计和临床应用,中国眼科学会眼底病学组与相关专家合作,在调查问题、讨论生产证据和举办指南研讨会后,制定了本指南。旨在为筛查系统的黄斑区域和病变体征的定义、人工智能采用场景、算法模型构建、数据集建立和标记、架构和功能设计以及图像数据采集建立统一的标准,以指导筛查工作的实施。
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The standardized design and application guidelines: A primary-oriented artificial intelligence screening system of the lesion sign in the macular region based on fundus color photography

With the popularity and development of artificial intelligence (AI), disease screening systems based on AI algorithms are gradually emerging in the medical field. Such systems can be used for primary screening of diseases to relieve the pressure on primary health care. In recent years, AI algorithms have demonstrated good performance in the analysis and identification of lesion signs in the macular region of fundus color photography, and a screening system for fundus lesion signs applicable to primary screening is bound to emerge in the future. Therefore, to standardize the design and clinical application of macular region lesion sign screening systems based on AI algorithms, the Ocular Fundus Diseases Group of Chinese Ophthalmological Society, in collaboration with relevant experts, developed this guideline after investigating issues, discussing production evidence, and holding guideline workshops. It aimed to establish uniform standards for the definition of the macular region and lesion signs, AI adoption scenarios, algorithm model construction, dataset establishment and labeling, architecture and function design, and image data acquisition for the screening system to guide the implementation of the screening work.

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来源期刊
Intelligent medicine
Intelligent medicine Surgery, Radiology and Imaging, Artificial Intelligence, Biomedical Engineering
CiteScore
5.20
自引率
0.00%
发文量
19
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