Zhang Gerui, Luo Lin, Zhang Limin, Liu Zhuo
Department of Critical Care Unit, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China.
Department of Critical Care Unit, The Second Hospital of Dalian Medical University, 467 Zhongshan Road, Shahekou District, Dalian 116023, China.
Diagnostics (Basel). 2023 Jan 18;13(3):357. doi: 10.3390/diagnostics13030357.
Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.
机器学习（ML）是一种基于大数据的算法，它通过分类、预测和优化从先前观察到的数据中学习模式，以完成特定任务。近年来，医学领域的机器学习发展迅速，包括肺部影像分析、重症医学监测、机械通气，以及插管病因预测评估、肺功能评估与预测、阻塞性睡眠呼吸暂停等生物信息监测等。机器学习可以有良好的表现，是一个极具潜力的工具，尤其是在间质性肺疾病的影像诊断方面。特发性肺纤维化（IPF）是呼吸系统疾病治疗中的一个主要问题，由于成纤维细胞异常增殖，导致肺组织破坏。诊断主要依赖于影像学的早期发现和早期治疗，这可以有效延长患者寿命。如果能够利用计算机辅助检查与纤维化效应相关的结果，及时诊断此类疾病对医生和患者都将具有重要价值。我们之前还提出了一种机器学习算法模型，该模型在特发性肺纤维化的早期影像预测中可以发挥良好的临床指导作用。目前，人工智能和机器学习在改变呼吸医学的许多方面具有巨大潜力和能力，是研究的重点和热点。人工智能需要成为一种无形、无缝且公正的辅助工具，以高效、有效且可接受的方式帮助患者和医生做出更好的决策。本文的目的是综述机器学习在呼吸系统疾病各个方面的当前应用，希望能为临床医生应用算法模型时提供一些帮助和指导。
