BACKGROUND 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. OBJECTIVES 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. METHODS 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, 57 other). RESULTS 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. CONCLUSIONS 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.

中文翻译：

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利用 Vision Transformer 模型提高心脏磁共振心脏淀粉样变性的诊断准确性。


背景 心脏磁共振 （CMR） 成像是诊断心脏淀粉样变性 （CA） 的重要诊断工具。然而，区分 CA 与心肌疾病的其他病因可能具有挑战性。目的 本研究的目的是开发并严格验证深度学习 （DL） 算法，以帮助使用电影和晚期钆增强 CMR 成像区分 CA。方法 使用 807 例因疑似浸润性疾病或肥厚型心肌病 （HCM） 而被转诊接受 CMR 的患者的回顾性队列开发了 DL 模型。确诊明确如下： 252 例 CA 患者，290 例 HCM 患者，265 例既非 CA 也不 HCM （其他）。该队列被 70/30 分为训练集和测试集。训练视觉转换器 （ViT） 模型主要是为了识别 CA。该模型在由 157 名因疑似浸润性疾病或 HCM 而被转诊接受 CMR 的患者（51 名 CA、49 名 HCM、57 名其他）的外部队列中进行了验证。结果 ViT 模型在内部测试数据集中实现了诊断准确性 （84.1%） 和 0.954 的曲线下面积。ViT 模型在外部测试集中进一步证明准确率为 82.8%，曲线下面积为 0.957。在临床报告为中等/高置信度 CA 诊断的研究中，ViT 模型达到 90% （n = 55/61），在内部队列中报告不确定、遗漏或错误诊断 CA 的研究中，ViT 模型达到 61.1% （n = 22/36）。当去除图像质量差、双重病症或临床意义 CA 诊断模糊的研究时，该队列的 DL 准确率提高到 79.1%。 结论仅使用电影和晚期钆增强 CMR 图像的 ViT 模型可以在区分 CA 与疑似心肌病的其他潜在病因方面实现高精度，尤其是在大型单一州卫生系统和外部 CA 队列中报告的人类诊断信心不确定的情况下。