Cockrum Joshua, Nakashima Makiya, Ammoury Carl, Rizkallah Diane, Mauch Joseph, Lopez David, Wolinksy David, Hwang Tae Hyun, Kapadia Samir, Svensson Lars G, Grimm Richard, Hanna Mazen, Tang W H Wilson, Nguyen Christopher, Chen David, Kwon Deborah
University of Michigan Hospitals, Ann Arbor, Michigan, USA.
Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovations Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
JACC Cardiovasc Imaging. 2025 Mar;18(3):278-290. doi: 10.1016/j.jcmg.2024.09.010. Epub 2024 Dec 4.
Cardiac magnetic resonance (CMR) imaging is an important diagnostic tool for diagnosis of cardiac amyloidosis (CA). However, discrimination of CA from other etiologies of myocardial disease can be challenging.
The aim of this study was to develop and rigorously validate a deep learning (DL) algorithm to aid in the discrimination of CA using cine and late gadolinium enhancement CMR imaging.
A DL model using a retrospective cohort of 807 patients who were referred for CMR for suspicion of infiltrative disease or hypertrophic cardiomyopathy (HCM) was developed. Confirmed definitive diagnosis was as follows: 252 patients with CA, 290 patients with HCM, and 265 with neither CA or HCM (other). This cohort was split 70/30 into training and test sets. A vision transformer (ViT) model was trained primarily to identify CA. The model was validated in an external cohort of 157 patients also referred for CMR for suspicion of infiltrative disease or HCM (51 CA, 49 HCM, and 57 other).
The ViT model achieved a diagnostic accuracy (84.1%) and an area under the curve of 0.954 in the internal testing data set. The ViT model further demonstrated an accuracy of 82.8% and an area under the curve of 0.957 in the external testing set. The ViT model achieved an accuracy of 90% (n = 55 of 61), among studies with clinical reports with moderate/high confidence diagnosis of CA, and 61.1% (n = 22 of 36) among studies with reported uncertain, missing, or incorrect diagnosis of CA in the internal cohort. DL accuracy of this cohort increased to 79.1% when studies with poor image quality, dual pathologies, or ambiguity of clinically significant CA diagnosis were removed.
A ViT model using only cine and late gadolinium enhancement CMR images can achieve high accuracy in differentiating CA from other underlying etiologies of suspected cardiomyopathy, especially in cases when reported human diagnostic confidence was uncertain in both a large single state health system and in an external CA cohort.
心脏磁共振成像（CMR）是诊断心脏淀粉样变性（CA）的重要诊断工具。然而，将CA与其他心肌病病因区分开来可能具有挑战性。
本研究的目的是开发并严格验证一种深度学习（DL）算法，以辅助使用电影成像和延迟钆增强CMR成像来鉴别CA。
使用807例因怀疑浸润性疾病或肥厚型心肌病（HCM）而接受CMR检查的患者的回顾性队列开发了一个DL模型。确诊的明确诊断如下：252例CA患者，290例HCM患者，265例既无CA也无HCM（其他）。该队列以70/30的比例分为训练集和测试集。主要训练了一个视觉Transformer（ViT）模型以识别CA。该模型在另一个157例因怀疑浸润性疾病或HCM而接受CMR检查的外部队列中进行了验证（51例CA，49例HCM，57例其他）。
ViT模型在内部测试数据集中的诊断准确率为84.1%，曲线下面积为0.954。ViT模型在外部测试集中进一步显示出82.8%的准确率和0.957的曲线下面积。在内部队列中，对于临床报告中CA诊断为中度/高度置信的研究，ViT模型的准确率为90%（61例中的55例），对于报告中CA诊断不确定、缺失或错误的研究，准确率为61.1%（36例中的22例）。当去除图像质量差、存在双重病变或临床显著CA诊断不明确的研究时，该队列的DL准确率提高到79.1%。
仅使用电影成像和延迟钆增强CMR图像的ViT模型在区分CA与其他疑似心肌病的潜在病因方面可达到高精度，特别是在大型单一州卫生系统及外部CA队列中报告的人类诊断置信度不确定的情况下。
