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. 2022 Feb 23;7(4):386–395. doi: 10.1001/jamacardio.2021.6059

Figure 1. Deep Learning Workflow Combining Evaluation of Ventricular Dimensions and Suspicion for Underdiagnosed Diseases.

Figure 1.

A, The deep learning algorithm used parasternal long-axis echocardiogram video as input to derive key points and estimate ventricular dimensions. After identifying patients with left ventricular hypertrophy (LVH), the deep learning workflow used a video-based architecture to distinguish common causes of LVH. B, Correlation of human annotations vs model predictions for ventricular dimensions in data sets from Stanford Health Care (SHC; n = 1200), Cedars-Sinai Medical Center (CSMC; n = 1309), and Unity Imaging Collaborative (n = 1791). C, Model variation on the 3 data sets vs human clinical annotation variation. Middle lines represent means; upper and lower bounds of the boxes, 25th and 75th percentiles; and points, values greater than 1.5 times the IQR. D, Receiver operating characteristic curves for diagnosis of amyloidosis in the SHC validation (n = 813) and test (n = 812) sets. AS indicates aortic stenosis; AUC, area under the curve; CA, cardiac amyloid; HCM, hypertrophic cardiomyopathy; HTN, hypertension; IVS, intraventricular septum; LVID, LV internal dimension; LVPW, LV posterior wall.