■点照护超声(POCUS)可直接在床边进行心脏成像,但受到简短采集的限制,采集质量的变化,缺乏先进的协议。
■为了开发和验证用于检测心脏POCUS上未诊断的心肌病的深度学习模型,利用一种新颖的收购质量适应性建模策略。
■要开发模型,我们确定了美国一家大型卫生系统中五家医院的患者的经胸超声心动图(TTE),这些患者患有转甲状腺素蛋白淀粉样心肌病(ATTR-CM,通过Tc99m-焦磷酸盐成像证实),肥厚型心肌病(HCM,经心脏磁共振证实),和对照丰富了严重AS的存在。在290,245个TTE视频样本中,我们使用新颖的增强方法和定制的损失函数来衡量图像和视图质量,以训练多标签,查看不可知的基于视频的卷积神经网络(CNN)来区分ATTR-CM的存在,HCM,和/或AS。从2011年到2023年,在1,330名独立急诊科(ED)患者的1,879项研究中,对3,758个现实世界的POCUS视频进行了测试。
■我们的多标签,在标准TTE研究中,视图不可知分类器在区分ATTR-CM(AUROC0.98[95CI:0.96-0.99])和HCM(AUROC0.95[95%CI:0.94-0.96])方面表现出最先进的性能.解剖视图正确性的自动化指标证实了POCUS与TTE视频的质量显着降低(中位视图分类器置信度为0.63[IQR:0.44-0.88]vs0.93[IQR:0.69-1.00],p<0.001)。部署到POCUS视频时,我们的算法有效区分ATTR-CM和HCM,AUROC高达0.94(胸骨旁长轴(PLAX)),和0.85(根尖4腔),对应的阳性诊断比值比分别为46.7和25.5。总的来说,POCUS队列中18/35(51.4%)的ATTR-CM和32/57(41.1%)的HCM患者在最终确认成像前一年出现AI阳性筛查。
■我们定义并验证了一个AI框架,该框架可以实现可扩展,使用POCUS对诊断不足的心肌病进行机会性筛查。
UNASSIGNED: Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We developed and tested artificial intelligence (AI) models to automate the detection of underdiagnosed cardiomyopathies from cardiac POCUS.
UNASSIGNED: In a development set of 290,245 transthoracic echocardiographic videos across the Yale-New Haven Health System (YNHHS), we used augmentation approaches and a customized loss function weighted for view quality to derive a POCUS-adapted, multi-label, video-based convolutional neural network (CNN) that discriminates HCM (hypertrophic cardiomyopathy) and ATTR-CM (transthyretin amyloid cardiomyopathy) from controls without known disease. We evaluated the final model across independent, internal and external, retrospective cohorts of individuals who underwent cardiac POCUS across YNHHS and Mount Sinai Health System (MSHS) emergency departments (EDs) (2011-2024) to prioritize key views and validate the diagnostic and prognostic performance of single-view screening protocols.
UNASSIGNED: We identified 33,127 patients (median age 61 [IQR: 45-75] years, n=17,276 [52·2%] female) at YNHHS and 5,624 (57 [IQR: 39-71] years, n=1,953 [34·7%] female) at MSHS with 78,054 and 13,796 eligible cardiac POCUS videos, respectively. An AI-enabled single-view screening approach successfully discriminated HCM (AUROC of 0·90 [YNHHS] & 0·89 [MSHS]) and ATTR-CM (YNHHS: AUROC of 0·92 [YNHHS] & 0·99 [MSHS]). In YNHHS, 40 (58·0%) HCM and 23 (47·9%) ATTR-CM cases had a positive screen at median of 2·1 [IQR: 0·9-4·5] and 1·9 [IQR: 1·0-3·4] years before clinical diagnosis. Moreover, among 24,448 participants without known cardiomyopathy followed over 2·2 [IQR: 1·1-5·8] years, AI-POCUS probabilities in the highest (vs lowest) quintile for HCM and ATTR-CM conferred a 15% (adj.HR 1·15 [95%CI: 1·02-1·29]) and 39% (adj.HR 1·39 [95%CI: 1·22-1·59]) higher age- and sex-adjusted mortality risk, respectively.
UNASSIGNED: We developed and validated an AI framework that enables scalable, opportunistic screening of treatable cardiomyopathies wherever POCUS is used.
UNASSIGNED: National Heart, Lung and Blood Institute, Doris Duke Charitable Foundation, BridgeBio.