ArticleJournal of imaging2023
Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 18 citations in OpenAlex.
- Curriculum-Based Deep Learning for Automated Segmental Left Ventricular Hypertrophy Classification Using Short-Axis Echocardiography.Diagnostics (Basel, Switzerland) · 2026Article
- Smarter FoCUS: AI-guided focused cardiac ultrasound enables novice detection of left ventricular dysfunction.European heart journal. Digital health · 2026Article
- Artificial Intelligence in Cardiac Amyloidosis: A State-of-the-Art Review.Journal of clinical medicine · 2026Review
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
- A unified multi-task learning framework for automated assessment of left ventricular structure and its systolic function from echocardiography.Scientific reports · 2025Article
- The Role of Artificial Intelligence in Cardiac Amyloidosis: A Focus on Diagnosis and Clinical Application.Journal of cardiovascular development and disease · 2025Review
- Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review.Journal of the American Heart Association · 2025Review
- Review
- Evolving Strategies in Cardiac Amyloidosis: From Mechanistic Discoveries to Diagnostic and Therapeutic Advances.Cardiology clinics · 2025Review
- Advancements and applications of artificial intelligence in cardiovascular imaging: a comprehensive review.European heart journal. Imaging methods and practice · 2024Review
- Review
- How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges.Journal of cardiovascular development and disease · 2024Review
- Challenges and solutions of echocardiography generalization for deep learning: a study in patients with constrictive pericarditis.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Article
- Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions.Discoveries (Craiova, Romania)Review
Corrections and comments
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Authors and funding
28 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsIncreased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. METHODS AND
resultsPatients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 and 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4-chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: HCM: 0.93, CA: 0.90, and HTN/other: 0.92).
conclusionThe echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.
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