Deep learning on routine coronary angiography detected any RV dysfunction with AUC 0.82 and significant RV dysfunction with AUC 0.83; combining ECG increased AUC to 0.87.
Does a deep learning model accurately detect right ventricular dysfunction from routine cine coronary angiography of the right coronary artery?
A novel deep learning algorithm can accurately detect right ventricular dysfunction from routine right coronary angiography cine images, with performance further enhanced by incorporating ECG data.
Absolute Event Rate: 0% vs 0%
Abstract Background Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sought to explore the role of deep learning (DL) in detecting right ventricular dysfunction from routinely acquired cine images during coronary angiography. Methods 3D-convolutional neural networks (CNN) were trained to identify right ventricle (RV) dysfunction from diagnostic angiography of the right coronary artery (RCA). The models’ input was two cine angiograms from LAO and RAO projections. The model’s output was normal vs. abnormal RV function. Ground truth on RV function was obtained from the nearest transthoracic echocardiogram to the coronary angiogram. Two analyses were performed to identify (a) any RV dysfunction (≥mild); and (b) significant RV dysfunction (≥mild-moderate). Results A total of 10,336 coronary angiograms from 9,849 patients (mean age 66, 36% women) were included. The cohort was split into training (70%), validation (15%), and testing (15%) sets. In the testing sets, the models attained an area under the ROC curve (AUC), sensitivity, and specificity of 0.82 (95% CI: 0.80-0.84), 0.75, and 0.74, for detecting any RV dysfunction, and 0.83 (95% CI: 0.80-0.86), 0.82, and 0.70 for detecting significant (≥mild-moderate) RV dysfunction. Combining an ECG-driven AI model with the angio-driven model improved the AUC values to 0.83 and 0.87 for detecting any or advanced RV dysfunction, respectively. Conclusion A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model’s performance improved further by adding ECG to its inputs.
Rostami et al. (Tue,) reported a other. Deep learning on routine coronary angiography detected any RV dysfunction with AUC 0.82 and significant RV dysfunction with AUC 0.83; combining ECG increased AUC to 0.87.
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