ArticleJACC. Advances2025
International Validation of Echocardiographic Artificial Intelligence Amyloid Detection Algorithm.
Article in JACC. Advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- The long road to recognition: a historical review of amyloidosis from early pathology to disease-modifying therapy.Heart failure reviews · 2026Review
- Myocardial Fibrosis in Hypertrophic Cardiomyopathy: Leveraging Quantitative Imaging and Artificial Intelligence for Precision Management.Reviews in cardiovascular medicine · 2026Review
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
- Behavioral Intelligence in Action: Implementation Science and Cardiovascular Learning Health Systems.JACC. Advances · 2025Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnosis of cardiac amyloidosis (CA) is often missed or delayed due to confusion with other causes of increased left ventricular wall thickness. Conventional transthoracic echocardiographic measurements like global longitudinal strain have shown promise in distinguishing CA, but with limited specificity.
objectivesWe conducted a multisite retrospective case-control study to investigate the performance of a computer vision algorithm for CA identification across multiple international sites.
methodsEchoNet-left ventricular hypertrophy (LVH) is a computer vision deep learning algorithm for the detection of CA based on parasternal long axis and apical-4-chamber view videos. We evaluated EchoNet-LVH's ability to distinguish between the echocardiogram studies of 574 CA patients and 979 controls. We reported discrimination performance with an area under the receiver operating characteristic curve and associated sensitivity, specificity, and positive predictive value at the prespecified threshold.
resultsEchoNet-LVH had an area under the receiver operating characteristic curve of 0.896 (95% CI: 0.875-0.916). At the prespecified model threshold optimizing for specificity, EchoNet-LVH had a sensitivity of 0.644 (95% CI: 0.601-0.685), specificity of 0.988 (95% CI: 0.978-0.994), positive predictive value of 0.968 (95% CI: 0.944-0.984), and negative predictive value of 0.828 (95% CI: 0.804-0.850). There was no evidence of heterogeneity in performance by site, race, sex, age, body mass index, CA subtype, or ultrasound manufacturer.
conclusionsEchoNet-LVH can assist with earlier and accurate diagnosis of CA. EchoNet-LVH achieved development goals to be highly specific to maximize positive predictive value of downstream confirmatory testing since CA is a rare disease.
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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.