目的:阻塞性气道疾病,包括哮喘和慢性阻塞性肺疾病(COPD),是两种最常见的慢性呼吸道健康问题。这两种情况都需要健康专业知识来进行诊断。因此,这一过程对于医疗保健提供者来说是耗时的,诊断质量受操作人员内部和操作人员之间的差异影响。在这项研究中,我们探讨了自动检测阻塞性气道疾病以降低成本并提高诊断质量的作用。
方法:我们调查了现有的证据,并将首选报告项目应用于系统审查和荟萃分析(PRISMA)指南,以搜索IEEE中的记录,谷歌学者,和PubMed数据库。我们确定了2013年至2022年发表的65篇论文,这些论文涵盖了67项不同的研究。审查过程是根据用于疾病检测的医学数据构建的。我们确定了六个主要类别,即空气流动,遗传,成像,信号,和杂项。对于每个类别,我们报告了两种疾病检测方法及其性能。
结果:我们发现,在14项综述的研究中,医学成像被用作自动阻塞性气道疾病检测的数据。在13项研究中使用了遗传和生理信号。在9项和7项研究中使用了医疗记录和气流,分别。大多数论文都是在2020年发表的,我们发现机器学习(ML)方面的工作是深度学习(DL)的三倍。统计分析表明,与ML相比,DL技术具有更高的准确性(ACC)。卷积神经网络(CNN)是最常见的DL分类器,支持向量机(SVM)是最广泛使用的ML分类器。在我们的审查中,我们仅发现了两个公开的哮喘和COPD数据集.大多数研究使用私人临床数据集,所以数据大小和数据组成是不一致的。
结论:我们的综述结果表明,人工智能(AI)可以提高健康专业人员在COPD和哮喘诊断过程中的决策质量和效率。然而,我们在这篇综述中发现了几个局限性,例如缺乏数据集的一致性,有限的数据集和远程监测没有得到充分探索.我们呼吁社会接受和信任计算机辅助的气流阻塞疾病诊断,并鼓励卫生专业人员与人工智能科学家密切合作,在临床实践和医院环境中促进自动检测。
OBJECTIVE: Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality.
METHODS: We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The
review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.
RESULTS: We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our
review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.
CONCLUSIONS: Our
review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this
review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.