Deep learning for transesophageal echocardiography view classification

Abstract

Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and intraprocedural TEE data is the complexity and unstructured nature of these images. In the present study, we developed a deep learning-based, multi-category TEE view classification model that can be used to add structure to intraoperative and intraprocedural TEE imaging data. More specifically, we trained a convolutional neural network (CNN) to predict standardized TEE views using labeled intraoperative and intraprocedural TEE videos from Cedars-Sinai Medical Center (CSMC). We externally validated our model on intraoperative TEE videos from Stanford University Medical Center (SUMC). Accuracy of our model was high across all labeled views. The highest performance was achieved for the Trans-Gastric Left Ventricular Short Axis View (area under the receiver operating curve [AUC] = 0.971 at CSMC, 0.957 at SUMC), the Mid-Esophageal Long Axis View (AUC = 0.954 at CSMC, 0.905 at SUMC), the Mid-Esophageal Aortic Valve Short Axis View (AUC = 0.946 at CSMC, 0.898 at SUMC), and the Mid-Esophageal 4-Chamber View (AUC = 0.939 at CSMC, 0.902 at SUMC). Ultimately, we demonstrate that our deep learning model can accurately classify standardized TEE views, which will facilitate further downstream deep learning analyses for intraoperative and intraprocedural TEE imaging.

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

“…Steffner K. et al pioneered the AI-based interpretation of TEE images by developing a convolutional neural network (CNN) to accurately identify standardized TEE views. This demonstration of effective AI classification opens the door to advanced deep learning analyses in intraoperative and intraprocedural TEE imaging [ 46 ].…”

Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction

“…AI is also being developed for transesophageal echocardiography (TEE). Steffner et al showed that AI could accurately be used to classify TEE views [55]. Yu and colleagues developed an algorithm for TEE to automatically calculate the mitral annular plane systolic excursion (MAPSE) which allows the evaluation of LV function.…”