Abstract
OBJECTIVES: To evaluate the performance of a deep learning-based method to automatically quantify left ventricular (LV) function from MR images in different cardiomyopathy. METHODS: This retrospective study included MRI data sets from 2013 to 2020. Data on left ventricular function from patients with hypertrophic cardiomyopathy (HCM), patients with dilated cardiomyopathy (DCM), and healthy participants were analyzed. MRI data from a total of 388 patients were measured manually and automatically.The performance of Convolutional Neural Networks (CNNs) was evaluated based on the manual notes of two experienced observers: (a) LV segmentation accuracy, and (b) LV functional parameter accuracy. Bland-Altman analysis, Receiver operating Characteristic (ROC) curve analysis and Pearson correlation analysis were used to evaluate the consistency between fully automatic and manual diagnosis of HCM and DCM. RESULTS: The deep-learning CNN performed best in HCM in evaluating LV function and worst in DCM. Compared with manual analysis, four parameters of LV function in the HCM group showed high correlation (r at least >0.901), and the correlation of DCM in all parameters was weaker than that of HCM, especially EF (r(2) = 0.776) and SV (r(2) = 0.645). ROC curve analysis indicated that at the optimal cut-off value, EF from automatic segmentation identified DCM and HCM patients with sensitivity of 92.31 and 78.05%, specificity of 82.96 and 54.07%, respectively. CONCLUSION: Among different heart diseases, the analysis of cardiac function based on deep-learning CNN may have different performances, with DCM performing the worst and HCM the best and thus, special attention should be paid to DCM patients when assessing LV function through artificial intelligence method. LV function parameter obtained by artificial intelligence method may play an important role in the future AI diagnosis of HCM and DCM. ADVANCES IN KNOWLEDGE: These data for the first time objectively evaluate the performance of a commercially available deep learning-based method in cardiac function evaluation of different cardiomyopathy and point out its advantages and disadvantages in different cardiomyopathy. This work did not attempt to design the algorithm itself, but rather applied an already existing method to a test dataset of clinical data and evaluated the results for a limited number of cardiomyopathy.