Key result
A deep convolutional neural network using left atrial curved M-mode speckle-tracking images achieved 85.29% accuracy in predicting atrial fibrillation recurrence after ablation, outperforming conventional logistic regression models.
Why the study?
Curved M-mode images of global strain and strain rate provide detailed spatiotemporal deformation data, but whether a deep convolutional neural network could accurately classify these images to predict outcomes after radiofrequency catheter ablation was unknown.
Does a deep convolutional neural network using left atrial curved M-mode speckle-tracking images improve the prediction of atrial fibrillation recurrence after ablation compared to traditional logistic regression models?
Cohort (n=326)
No
Does a deep convolutional neural network using left atrial curved M-mode speckle-tracking images improve the prediction of atrial fibrillation recurrence after ablation compared to traditional logistic regression models?
A deep convolutional neural network utilizing left atrial curved M-mode speckle-tracking images can accurately predict atrial fibrillation recurrence after radiofrequency catheter ablation, outperforming traditional logistic regression models.
May support CNN-based strain analysis for post-ablation AF stratification; hypothesis-generating and requires prospective validation before any clinical use.
Aims: Curved M-mode images of global strain (GS) and strain rate (GSR) provide sufficiently detailed spatiotemporal information of deformation mechanics. This study investigated whether a deep convolutional neural network (CNN) could accurately classify these images in patients with atrial fibrillation (AF) who underwent radiofrequency catheter ablation (RFCA) with different outcomes. Methods and Results: We retrospectively evaluated 606 consecutive patients who underwent RFCA for drug-refractory AF. Patients were divided into AF-free ( n = 443) and AF-recurrent ( n = 163) groups. Transthoracic echocardiography was performed within 24 h after RFCA. Left atrial curved M-mode speckle-tracking images were acquired from randomly selected 163 patients in AF-free group and 163 patients in AF-recurrent group as the dataset for deep CNN modeling. We used the ReLu activation function and repeatedly performed CNN model for 32 times to evaluate the stability of hyperparameters. Logistic regression models with the left atrial dimension, emptying fraction, and peak systolic GS as predictor variables were used for comparisons. Images from the apical 2-chamber (2-C) and 4-chamber (4-C) views had distinct features, leading to different CNN performance between settings; of them, the “4-C GS+4-C GSR” setting provided the highest performance index values. All four predictor variables used for logistic regression modeling were significant; however, none of them, individually or in any combined form, could outperform the optimal CNN model. Conclusion: The novel approach using deep CNNs for learning features of left atrial curved M-mode speckle-tracking images seems to be optimal for classifying outcome status after AF ablation.
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Hwang et al. (2021) conducted a cohort in Atrial Fibrillation (n=326). Deep convolutional neural network (CNN) using left atrial curved M-mode speckle-tracking images vs. Logistic regression models using left atrial dimension, emptying fraction, and peak systolic global strain was evaluated on Accuracy of predicting atrial fibrillation recurrence after ablation. A deep convolutional neural network using left atrial curved M-mode speckle-tracking images achieved 85.29% accuracy in predicting atrial fibrillation recurrence after ablation, outperforming conventional logistic regression models.
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