ArticleScientific reports2024
Deep learning for transesophageal echocardiography view classification.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 27 citations in OpenAlex.
- Artificial intelligence in assisted localization of fetal ultrasound standard planes: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Artificial intelligence-based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.Journal of cardiovascular imaging · 2026Article
- Advances in cardiac devices and bioelectronics augmented with artificial intelligence.The Journal of physiology · 2026Review
- Proof-of-Concept Automated Framework for Intraoperative Transesophageal Echocardiography: View Classification and Biventricular Function Assessment.Journal of cardiothoracic and vascular anesthesia · 2026Article
- From innovation to implementation: Addressing the AI adoption gap in cardiac surgery.American heart journal plus : cardiology research and practice · 2026Review
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Automated evaluation for pericardial effusion and cardiac tamponade with echocardiographic artificial intelligence.European heart journal. Digital health · 2025Article
- Point-of-Care Transesophageal Echocardiography in Emergency and Intensive Care: An Evolving Imaging Modality.Biomedicines · 2025Review
- Contemporary applications of artificial intelligence and machine learning in echocardiography.NPJ cardiovascular health · 2025Review
- Diagnostic value of transesophageal echocardiography on morphology and function of left atrial appendage in patients with unclosed foramen ovale complicated with atrial fibrillation.BMC cardiovascular disorders · 2025Article
- High-Throughput Deep Learning Detection of Mitral Regurgitation.Circulation · 2024Article
- Multimodal Cardiac Imaging Revisited by Artificial Intelligence: An Innovative Way of Assessment or Just an Aid?Cureus · 2024Review
- Future Horizons: The Potential Role of Artificial Intelligence in Cardiology.Journal of personalized medicine · 2024Review
- Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology.Journal of cardiothoracic and vascular anesthesia · 2024Review
- Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- Emerging Role of Artificial Intelligence in Echocardiography.Annals of cardiac anaesthesia · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.