Hatfaludi Cosmin-Andrei, Roșca Aurelian, Popescu Andreea Bianca, Chitiboi Teodora, Sharma Puneet, Benedek Theodora, Itu Lucian Mihai
Advanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.
Automation and Information Technology, Transilvania University of Brasov, Mihai Viteazu nr. 5, Brasov, 5000174, Romania.
Int J Cardiovasc Imaging. 2024 Dec;40(12):2617-2629. doi: 10.1007/s10554-024-03284-8. Epub 2024 Nov 7.
Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipitating critical outcomes including heart failure and arrhythmias. This investigation primarily aims to identify the optimal cardiovascular magnetic resonance imaging (CMRI) views for distinguishing between normal and myocarditis cases, using deep learning (DL) methodologies. Analyzing CMRI data from a cohort of 269 individuals, with 231 confirmed myocarditis cases and 38 as control participants, we implemented an innovative DL framework to facilitate the automated detection of myocarditis. Our approach was divided into single-frame and multi-frame analyses to evaluate different views and types of acquisitions for optimal diagnostic accuracy. The results demonstrated a weighted accuracy of 96.9%, with the highest accuracy achieved using the late gadolinium enhancement (LGE) 2-chamber view, underscoring the potential of DL in distinguishing myocarditis from normal cases on CMRI data.
心肌炎以心肌组织炎症为特征，对心血管功能构成重大风险，可能引发包括心力衰竭和心律失常在内的严重后果。本研究主要旨在使用深度学习（DL）方法确定区分正常病例和心肌炎病例的最佳心血管磁共振成像（CMRI）视图。通过分析来自269名个体的CMRI数据，其中231例为确诊的心肌炎病例，38例为对照参与者，我们实施了一个创新的DL框架来促进心肌炎的自动检测。我们的方法分为单帧和多帧分析，以评估不同的视图和采集类型，以实现最佳诊断准确性。结果显示加权准确率为96.9%，使用钆延迟增强（LGE）二腔视图获得的准确率最高，这突出了DL在CMRI数据上区分心肌炎和正常病例的潜力。
