Boyd Chris, Brown Greg, Kleinig Timothy, Dawson Joseph, McDonnell Mark D, Jenkinson Mark, Bezak Eva
Allied Health and Human Performance, University of South Australia, SA 5000, Australia.
South Australia Medical Imaging, Adelaide, SA 5000, Australia.
Diagnostics (Basel). 2021 Mar 19;11(3):551. doi: 10.3390/diagnostics11030551.
Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.
针对临床血管分析的机器学习（ML）研究，例如对中风和冠状动脉疾病有用的研究，在成像方式和血管区域之间差异很大。获取大量多样的患者成像数据集存在困难，以及特定方法缺乏透明度，都是进一步发展的障碍。本文综述了定量血管ML的现状，确定了所有成像方式共有的优缺点。2021年1月从MEDLINE和Scopus数据库搜索中系统收集了过去8年的文献。对满足所有搜索标准（包括至少50名患者）的论文进行了进一步分析，并提取了相关数据，共47篇出版物。与专家手动分析或侵入性定量相比，当前的ML图像分割、疾病风险预测和病理定量方法已显示出超过70%的敏感性和特异性。尽管如此，方法学和结果报告的不一致阻碍了模型间的比较，妨碍了识别最具潜力的方法。这项技术的临床潜力在冠状动脉疾病的计算机断层扫描中得到了充分证明，但在其他方式和身体区域实际上仍然有限，特别是由于缺乏常规侵入性参考测量和患者数据集。
