Pompe Esther, Kwee Anastasia Kal, Tejwani Vickram, Siddharthan Trishul, Mohamed Hoesein Firdaus Aa
Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands.
Respiratory Institute, Cleveland Clinic (VT), USA.
Respir Med. 2023 Mar;208:107130. doi: 10.1016/j.rmed.2023.107130. Epub 2023 Jan 23.
Asthma is a common disorder affecting around 315 million individuals worldwide. The heterogeneity of asthma is becoming increasingly important in the era of personalized treatment and response assessment. Several radiological imaging modalities are available in asthma including chest x-ray, computed tomography (CT) and magnetic resonance imaging (MRI) scanning. In addition to qualitative imaging, quantitative imaging could play an important role in asthma imaging to identify phenotypes with distinct disease course and response to therapy, including biologics. MRI in asthma is mainly performed in research settings given cost, technical challenges, and there is a need for standardization. Imaging analysis applications of artificial intelligence (AI) to subclassify asthma using image analysis have demonstrated initial feasibility, though additional work is necessary to inform the role of AI in clinical practice.
哮喘是一种常见疾病,全球约有3.15亿人受其影响。在个性化治疗和反应评估时代,哮喘的异质性变得越来越重要。哮喘的影像学检查方法有多种,包括胸部X线、计算机断层扫描(CT)和磁共振成像(MRI)扫描。除了定性成像外,定量成像在哮喘成像中也可发挥重要作用,以识别具有不同病程和对治疗(包括生物制剂)反应的表型。由于成本、技术挑战以及标准化需求,哮喘的MRI检查主要在研究环境中进行。利用人工智能(AI)进行图像分析对哮喘进行亚分类的影像学分析应用已显示出初步可行性,不过还需要更多工作来明确AI在临床实践中的作用。