The deep learning algorithm achieved high accuracy in measuring 16-segment left ventricular wall thickness, with mean absolute errors within clinically tolerable limits of 2mm.
Does a deep learning algorithm accurately segment the left ventricle and measure 16-segment wall thickness compared to manual expert annotation and anatomical specimens?
A novel deep learning algorithm can accurately and automatically segment the left ventricle and measure 16-segment wall thickness, offering a reliable and efficient tool for assessing left ventricular hypertrophy.
Absolute Event Rate: 0% vs 0%
Objectives: The quantitative analysis of 16-segment left ventricular wall thickness can provide insights into the pathological progression of left ventricular hypertrophy, influencing diagnosis and treatment.However, its applications were time-consuming and had large variability.This study developed an automatic measurement of 16segment left ventricular wall thickness based on left ventricular segmentation and validated in vivo and in vitro.Materials and Methods: The deep learning algorithm was trained and validated on 92,984 parasternal short-axis echocardiograms in a normal wall thickness cohort.Its performance was evaluated in an increased wall thickness dataset (26,523 echocardiograms) and an in vitro dataset (2238 echocardiograms) without additional adjustments.The primary outcomes were the performance of left ventricular segmentation and the accuracy in measuring 16-segment left ventricular wall thickness.Results: The study included 197 patients: 155 with normal wall thickness and 42 with increased wall thickness.Hypertension and nephropathy were common in the cohort with increased wall thickness.In the normal wall thickness cohort, the deep learning algorithm achieved similar accuracy to manual left ventricular myocardium segmentation (the mean dice similarity coefficient of 0.87, mean Hausdorff distance of 4.56mm) and accurately measured the 16-segment wall thickness (the mean absolute error from 0.31mm to 0.88mm).In the increased cohort, the mean dice similarity coefficient and Hausdorff distance of segmentation were 0.87 and 4.81mm, and the mean absolute errors of 16-segment wall thickness were within the clinically tolerable threshold of 2mm.In the in vitro cohort, the mean absolute error of segmental wall thickness was 1.0mm.Conclusions: Deep learning-derived left ventricular myocardial contours and accurate segmentation were feasible, which may provide a method for assessing left ventricular myocardium.Additionally, the method produced accurate measures of 16-segment wall thickness that were verified in vivo and in vitro, which may provide fundamental and clinical applications for the accurate diagnosis of left ventricular hypertrophy.
Peng et al. (Sun,) reported a other. The deep learning algorithm achieved high accuracy in measuring 16-segment left ventricular wall thickness, with mean absolute errors within clinically tolerable limits of 2mm.