Importance Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis. Objective To assess the accuracy of a deep learning workflow in quantifying ventricular hypertrophy and predicting the cause of increased LV wall thickness. Design, Settings, and Participants This cohort study included physician-curated cohorts from the Stanford Amyloid Center and Cedars-Sinai Medical Center (CSMC) Advanced Heart Disease Clinic for cardiac amyloidosis and the Stanford Center for Inherited Cardiovascular Disease and the CSMC Hypertrophic Cardiomyopathy Clinic for hypertrophic cardiomyopathy from January 1, 2008, to December 31, 2020. The deep learning algorithm was trained and tested on retrospectively obtained independent echocardiogram videos from Stanford Healthcare, CSMC, and the Unity Imaging Collaborative. Main Outcomes and Measures The main outcome was the accuracy of the deep learning algorithm in measuring left ventricular dimensions and identifying patients with increased LV wall thickness diagnosed with hypertrophic cardiomyopathy and cardiac amyloidosis. Results The study included 23 745 patients: 12 001 from Stanford Health Care (6509 [54.2%] female; mean [SD] age, 61.6 [17.4] years) and 1309 from CSMC (808 [61.7%] female; mean [SD] age, 62.8 [17.2] years) with parasternal long-axis videos and 8084 from Stanford Health Care (4201 [54.0%] female; mean [SD] age, 69.1 [16.8] years) and 2351 from CSMS (6509 [54.2%] female; mean [SD] age, 69.6 [14.7] years) with apical 4-chamber videos. The deep learning algorithm accurately measured intraventricular wall thickness (mean absolute error [MAE], 1.2 mm; 95% CI, 1.1-1.3 mm), LV diameter (MAE, 2.4 mm; 95% CI, 2.2-2.6 mm), and posterior wall thickness (MAE, 1.4 mm; 95% CI, 1.2-1.5 mm) and classified cardiac amyloidosis (area under the curve [AUC], 0.83) and hypertrophic cardiomyopathy (AUC, 0.98) separately from other causes of LV hypertrophy. In external data sets from independent domestic and international health care systems, the deep learning algorithm accurately quantified ventricular parameters (domestic: R2, 0.96; international: R2, 0.90). For the domestic data set, the MAE was 1.7 mm (95% CI, 1.6-1.8 mm) for intraventricular septum thickness, 3.8 mm (95% CI, 3.5-4.0 mm) for LV internal dimension, and 1.8 mm (95% CI, 1.7-2.0 mm) for LV posterior wall thickness. For the international data set, the MAE was 1.7 mm (95% CI, 1.5-2.0 mm) for intraventricular septum thickness, 2.9 mm (95% CI, 2.4-3.3 mm) for LV internal dimension, and 2.3 mm (95% CI, 1.9-2.7 mm) for LV posterior wall thickness. The deep learning algorithm accurately detected cardiac amyloidosis (AUC, 0.79) and hypertrophic cardiomyopathy (AUC, 0.89) in the domestic external validation site. Conclusions and Relevance In this cohort study, the deep learning model accurately identified subtle changes in LV wall geometric measurements and the causes of hypertrophy. Unlike with human experts, the deep learning workflow is fully automated, allowing for reproducible, precise measurements, and may provide a foundation for precision diagnosis of cardiac hypertrophy.

中文翻译：

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左心室肥大的高通量精确表型与心血管深度学习

重要性 左心室 (LV) 壁厚增加的早期检测和表征可显着影响患者护理，但受限于对肥厚、测量误差和变异性的认识不足，以及难以区分壁厚增加的原因，如肥厚、心肌病和心肌淀粉样变性。目的 评估深度学习工作流程在量化心室肥大和预测 LV 壁厚增加原因方面的准确性。设计，设置，从 1 月开始，这项队列研究包括来自斯坦福淀粉样蛋白中心和 Cedars-Sinai 医学中心 (CSMC) 心脏淀粉样变性高级心脏病诊所以及斯坦福遗传性心血管疾病中心和 CSMC 肥厚性心肌病肥厚性心肌病诊所的医生管理队列2008 年 1 月至 2020 年 12 月 31 日。对深度学习算法进行了训练和测试，回顾性地从斯坦福医疗保健公司、CSMC 和 Unity Imaging Collaborative 获得了独立的超声心动图视频。主要成果和措施主要成果是深度学习算法在测量左心室尺寸和识别诊断为肥厚性心肌病和心脏淀粉样变性的 LV 壁厚度增加的患者方面的准确性。结果 该研究包括 23745 名患者：12001 名来自斯坦福医疗保健（6509 名 [54.2%] 女性；平均 [SD] 年龄，61.6 [17.4] 岁）和 1309 名来自 CSMC（808 名 [61.7%] 女性；平均 [SD]年龄，62.8 [17.2] 岁）有胸骨旁长轴视频，8084 人来自斯坦福医疗保健（4201 [54.0%] 女性；平均 [SD] 年龄，69.1 [16.8] 岁）和 2351 人来自 CSMS（6509 [54.2%]女性；平均 [SD] 年龄，69.6 [14.7] 岁）有顶端 4 腔视频。深度学习算法准确测量了心室内壁厚度（平均绝对误差 [MAE]，1.2 毫米；95% CI，1.1-1.3 毫米）、左室直径（MAE，2.4 毫米；95% CI，2.2-2.6 毫米）和后壁壁厚（MAE，1.4 毫米；95% CI，1.2-1.5 毫米）和分类的心脏淀粉样变性（曲线下面积 [AUC]，0.83）和肥厚性心肌病（AUC，0.98）与 LV 肥厚的其他原因分开。在来自国内外独立医疗保健系统的外部数据集中，深度学习算法准确地量化了心室参数（国内：R2，0.96；国际：R2，0.90）。对于国内数据集，室间隔厚度的 MAE 为 1.7 mm（95% CI，1.6-1.8 mm），LV 内部尺寸为 3.8 mm（95% CI，3.5-4.0 mm），LV 内部尺寸为 1.8 mm（95% CI , 1.7-2.0 mm) 用于 LV 后壁厚度。对于国际数据集，室间隔厚度的 MAE 为 1.7 毫米（95% CI，1.5-2.0 毫米），左室内部尺寸为 2.9 毫米（95% CI，2.4-3.3 毫米），2.3 毫米（95% CI） , 1.9-2.7 mm) 用于 LV 后壁厚度。深度学习算法在国内外部验证站点准确检测出心脏淀粉样变性（AUC，0.79）和肥厚型心肌病（AUC，0.89）。结论和相关性 在这项队列研究中，深度学习模型准确地识别了 LV 壁几何测量的细微变化和肥大的原因。与人类专家不同，深度学习工作流程是完全自动化的，允许进行可重复的精确测量，并可能为心脏肥大的精确诊断奠定基础。