ArticlemedRxiv : the preprint server for health sciences2024
International Validation of Echocardiographic AI Amyloid Detection Algorithm.
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges.Current atherosclerosis reports · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Background: Diagnosis 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 (GLS) has shown promise in distinguishing CA, but with limited specificity. We conducted a study to investigate the performance of a computer vision detection algorithm in across multiple international sites. Methods: EchoNet-LVH is a computer vision deep learning algorithm for the detection of cardiac amyloidosis based on parasternal long axis and apical-4-chamber view videos. We conducted a multi-site retrospective case-control study evaluating EchoNet-LVH's ability to distinguish between the echocardiogram studies of CA patients and controls. We reported discrimination performance with area under the receiver operating characteristic curve (AUC) and associated sensitivity, specificity, and positive predictive value at the pre-specified threshold. Results: EchoNet-LVH had an AUC of 0.896 (95% CI 0.875 - 0.916). At pre-specified model threshold, EchoNet-LVH had a sensitivity of 0.644 (95% CI 0.601 - 0.685), specificity of 0.988 (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 minimal heterogeneity in performance by site, race, sex, age, BMI, CA subtype, or ultrasound manufacturer. Conclusion: EchoNet-LVH can assist with earlier and accurate diagnosis of CA. As CA is a rare disease, EchoNet-LVH is highly specific in order to maximize positive predictive value. Further work will assess whether early diagnosis results in earlier initiation of treatment in this underserved population.
Identifiers
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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.