De Ramón Fernández Alberto, Ruiz Fernández Daniel, Gilart Iglesias Virgilio, Marcos Jorquera Diego
Department of Computer Technology, University of Alicante, Alicante 03690, Spain.
Department of Computer Technology, University of Alicante, Alicante 03690, Spain.
Int J Med Inform. 2021 Nov 9;158:104640. doi: 10.1016/j.ijmedinf.2021.104640.
Chronic obstructive pulmonary disease (COPD) is a disease that causes airflow limitation to the lungs and has a high morbidity around the world. The objective of this study was to evaluate how artificial intelligence (AI) is being applied for the management of the disease, analyzing the objectives that are raised, the algorithms that are used and what results they offer.
We conducted a scoping review following the Arksey and O'Malley (2005) and Levac et al. (2010) guidelines. Two reviewers independently searched, analyzed and extracted data from papers of five databases: Web of Science, PubMed, Scopus, Cinahl and Cochrane. To be included, the studies had to apply some AI techniques for the management of at least one stage of the COPD clinical process. In the event of any discrepancy between both reviewers, the criterion of a third reviewer prevailed.
380 papers were identified through database searches. After applying the exclusion criteria, 67 papers were included in the study. The studies were of a different nature and pursued a wide range of objectives, highlighting mainly those focused on the identification, classification and prevention of the disease. Neural nets, support vector machines and decision trees were the AI algorithms most commonly used. The mean and median values of all the performance metrics evaluated were between 80% and 90%.
The results obtained show a growing interest in the development of medical applications that manage the different phases of the COPD clinical process, especially predictive models. According to the performance shown, these models could be a useful complementary tool in the decision-making by health specialists, although more high-quality ML studies are needed to endorse the findings of this study.
慢性阻塞性肺疾病（COPD）是一种导致肺部气流受限的疾病，在全球发病率很高。本研究的目的是评估人工智能（AI）如何应用于该疾病的管理，分析提出的目标、使用的算法及其提供的结果。
我们按照Arksey和O'Malley（2005年）以及Levac等人（2010年）的指南进行了一项范围综述。两名评审员独立地从五个数据库（Web of Science、PubMed、Scopus、Cinahl和Cochrane）的论文中搜索、分析和提取数据。纳入的研究必须应用一些AI技术来管理COPD临床过程的至少一个阶段。如果两名评审员之间存在任何差异，则以第三名评审员的标准为准。
通过数据库搜索识别出380篇论文。应用排除标准后，67篇论文被纳入研究。这些研究性质各异，追求广泛的目标，主要突出那些侧重于疾病识别、分类和预防的目标。神经网络、支持向量机和决策树是最常用的AI算法。所有评估的性能指标的平均值和中位数在80%至90%之间。
获得的结果表明，人们对开发管理COPD临床过程不同阶段的医学应用，尤其是预测模型，越来越感兴趣。根据所示的性能，这些模型可能是健康专家决策中有用的辅助工具，尽管需要更多高质量的机器学习研究来支持本研究的结果。
