Rau Alexander, Soschynski Martin, Taron Jana, Ruile Philipp, Schlett Christopher L, Bamberg Fabian, Krauss Tobias
Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland.
Klinik für Klinik für Kardiologie und Angiologie, Universitäts-Herzzentrum Freiburg - Bad Krozingen, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Bad Krozingen, Deutschland.
Radiologie (Heidelb). 2022 Nov;62(11):947-953. doi: 10.1007/s00117-022-01060-0. Epub 2022 Aug 25.
CLINICAL/METHODICAL ISSUE: Cardiac diseases are the leading cause of death. Many diseases can be specifically treated once a valid diagnosis is established. Cardiac magnetic resonance imaging (MRI) plays a central role in the workup of many cardiac pathologies. However, image acquisition as well as interpretation and related secondary image evaluation are time-consuming and complex.
Cardiac MRI is becoming increasingly established in international guidelines for the evaluation of cardiac function and differential diagnosis of a wide variety of cardiac diseases.
Cardiac MRI has limited reproducibility due to the acquisition technique and interpretation of findings with complex secondary measurements. Artificial intelligence techniques and radiomics offer the potential to improve the acquisition, interpretation, and reproducibility of cardiac MRI.
Research suggests that artificial intelligence and radiomic analysis can improve cardiac MRI in terms of image acquisition and also diagnostic and prognostic value. Furthermore, the implementation of artificial intelligence and radiomics may result in the identification of new biomarkers.
The implementation of artificial intelligence in cardiac MRI has great potential. However, the current level of evidence is still limited in some aspects; in particular there are too few prospective and large multicenter studies available. As a result, the algorithms developed are often not sufficiently validated scientifically and are not yet applied in clinical routine.
临床/方法学问题:心脏疾病是主要死因。一旦确立有效诊断,许多疾病就能得到针对性治疗。心脏磁共振成像(MRI)在许多心脏疾病的检查中起着核心作用。然而,图像采集以及解读和相关的二次图像评估既耗时又复杂。
心脏MRI在评估心脏功能和对多种心脏疾病进行鉴别诊断的国际指南中越来越得到认可。
由于采集技术以及对带有复杂二次测量结果的解读,心脏MRI的可重复性有限。人工智能技术和放射组学有潜力改善心脏MRI的采集、解读和可重复性。
研究表明,人工智能和放射组学分析在图像采集以及诊断和预后价值方面可以改善心脏MRI。此外,人工智能和放射组学的应用可能会带来新生物标志物的发现。
在心脏MRI中应用人工智能有很大潜力。然而,目前的证据水平在某些方面仍然有限;特别是前瞻性和大型多中心研究太少。因此,所开发的算法往往没有得到充分的科学验证,尚未应用于临床常规。