ArticleEuropean heart journal2025
Cardiac amyloidosis detection from a single echocardiographic video clip: a novel artificial intelligence-based screening tool.
Article in European heart journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study.PLOS digital health · 2026Article
- Diagnostic Performance Evaluation of Clinical and Artificial Intelligence Risk Models in Patients Referred for Cardiac Amyloidosis Testing.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2026Article
- Fully automated analysis of cardiotoxicity surveillance echocardiograms: a direct test-retest precision comparison with experts.European heart journal. Imaging methods and practice · 2026Article
- The Future of Imaging in Heart Failure: Toward Precision Phenotyping, Integration, and Intelligence.Current heart failure reports · 2026Review
- A CNN-based liver ultrasound plane recognition system with multimodal large language model-generated interpretable feedback.Abdominal radiology (New York) · 2026Article
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Imaging Advances in Light Chain Amyloidosis.Diagnostics (Basel, Switzerland) · 2026Review
- Cardio amyloid-artificial intelligence: advanced multi-modal screening for transthyretin cardiac amyloidosis in severe aortic stenosis patients.European heart journal. Digital health · 2026Article
- Impact of cardiac amyloidosis on survival in aortic stenosis patients undergoing TAVR: a systematic review and reconstructed time-to-event meta-analysis.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Article
- Diagnostic Performance of the Mayo ATTR-CM Score in Diverse Populations: Comparison With the AMY Score.JACC. Advances · 2026Article
- Two Decades of Light-Chain Amyloidosis: Real-World Trends in Diagnosis, Treatment, and Survival.Journal of hematology · 2026Article
- Promise and Practical Limits of Multimodal AI for Cardiac Amyloidosis Detection.Circulation. Cardiovascular imaging · 2026Article
- Artificial Intelligence in Cardiac Amyloidosis: A State-of-the-Art Review.Journal of clinical medicine · 2026Review
- Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence.Circulation. Cardiovascular imaging · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
- Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.Circulation · 2025Article
Corrections and comments
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Authors and funding
46 authors.
Funding
Abstract
BACKGROUND AND
aimsAccurate differentiation of cardiac amyloidosis (CA) from phenotypic mimics remains challenging using current clinical and echocardiographic techniques. The accuracy of a novel artificial intelligence (AI) screening algorithm for echocardiography-based CA detection was assessed.
methodsUtilizing a multisite, multiethnic dataset (n = 2612, 52% CA), a convolutional neural network was trained to differentiate CA from phenotypic controls using transthoracic apical four-chamber video clips. External validation was conducted globally across 18 sites including 597 CA cases and 2122 controls. Classification accuracy was assessed on the entire external validation dataset, and subgroup analyses were performed both on technetium pyrophosphate scintigraphy referrals, and individuals matched for age, sex, and wall thickness. Model accuracy was also compared with the transthyretin CA score and the increased wall thickness score within a subset of older heart failure with preserved ejection fraction patients with increased wall thickness.
resultsCardiac amyloidosis patients and controls displayed similar age, sex, race, and comorbidities. After the removal of uncertain AI predictions (13%), model discrimination and classification were excellent for the entire external validation dataset [area under the receiver operating characteristic curve (AUROC) 0.93, sensitivity 85%, specificity 93%], irrespective of CA subtype (sensitivity: light-chain = 84%, wild-type transthyretin = 85%, and hereditary transthyretin = 86%). Performance was maintained in subgroup analysis in patients clinically referred for technetium pyrophosphate scintigraphy imaging (AUROC 0.86, sensitivity 77%, specificity 86%) and matched patients (AUROC 0.92, sensitivity 84%, specificity 91%). The AI model (AUROC 0.93) also outperformed transthyretin CA score (AUROC 0.73) and increased wall thickness (AUROC 0.80) scores.
conclusionsThis AI screening model-using only an apical four-chamber view-effectively differentiated CA from other causes of increased left ventricular wall thickness.
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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.