AI for Aortic Stenosis
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial tests new AI technology to help detect two heart conditions: aortic stenosis (AS) and diastolic dysfunction (DD). These conditions can hinder the heart's ability to pump blood properly. The trial uses AI to analyze the results of a regular heart test, called an electrocardiogram (ECG), and then verifies the findings with an ultrasound if the AI detects an issue. The goal is to determine if this AI tool can simplify and speed up the diagnosis of these heart problems, particularly in areas with limited access to advanced medical care. This trial suits individuals aged 60 and up who have a scheduled ECG. As a Phase 2 trial, the research focuses on evaluating the AI tool's effectiveness in an initial, smaller group, offering participants a chance to contribute to innovative heart care solutions.
Do I need to stop my current medications for the trial?
The trial information does not specify whether you need to stop taking your current medications.
What prior data suggests that these AI-ECG models and POCUS are safe for diagnosing aortic stenosis and diastolic dysfunction?
Research has shown that the AI-enabled ECG system, part of the AI-ECG Dashboard, can quickly alert doctors to serious heart issues. Studies indicate it functions effectively without causing significant problems, confirming its general safety.
Regarding the point-of-care ultrasound (POCUS) used in this trial, research confirms its safety. It enables doctors to examine the body without adding extra risk to patients. Studies have found no increase in harm from using POCUS.
Overall, both the AI-ECG and POCUS have been used in healthcare without major safety concerns, suggesting they are safe for patients.12345Why are researchers excited about this trial?
Researchers are excited about this trial because it explores the use of artificial intelligence (AI) in diagnosing aortic stenosis and diastolic dysfunction in an outpatient setting. Unlike traditional diagnostic methods that rely heavily on human interpretation of electrocardiograms (ECGs) and other imaging techniques, this AI-based approach could enhance accuracy and efficiency in detecting these heart conditions. The use of AI holds the promise of earlier detection and potentially more personalized treatment plans, which could lead to better patient outcomes.
What evidence suggests that these AI-ECG models are effective for detecting aortic stenosis and diastolic dysfunction?
Studies have shown that AI-enabled electrocardiogram (AI-ECG) models hold promise for detecting heart issues. For aortic stenosis (AS), the AI-ECG correctly identifies the condition in 78 out of 100 cases and accurately rules it out in 74 out of 100 cases. For diastolic dysfunction (DD), it correctly identifies the condition in 83 out of 100 cases and rules it out in 80 out of 100 cases, demonstrating strong accuracy. Additionally, research has found that AI-ECG systems can surpass doctors in spotting irregular heart rhythms. These findings suggest that AI-ECG could be a valuable tool for diagnosing heart conditions, especially in areas with limited medical resources. Participants in this trial will use the AI-ECG Dashboard as part of their outpatient electrocardiogram (ECG) at the Mayo Clinic.678910
Who Is on the Research Team?
Jae Oh, M.D.
Principal Investigator
Mayo Clinic
Are You a Good Fit for This Trial?
Inclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Baseline Assessment
Participants undergo AI-enabled electrocardiograms to detect aortic stenosis and diastolic dysfunction
Diagnostic Evaluation
Participants with positive AI-ECG results receive a focused point-of-care ultrasound
Follow-up
Participants are monitored for safety and effectiveness after diagnostic evaluation
What Are the Treatments Tested in This Trial?
Interventions
- AI-ECG Dashboard
- Point of care ultrasound (POCUS)
How Is the Trial Designed?
1
Treatment groups
Experimental Treatment
Find a Clinic Near You
Who Is Running the Clinical Trial?
Mayo Clinic
Lead Sponsor
Citations
Investigating the Efficacy of AI-Powered Innovations in ...
The AI algorithms achieve a detection accuracy of 96-97% in identifying irregular heart rhythms and left ventricular hypertrophy and outperform ...
Economic analysis of an AI-enabled ECG alert system
Results of this economic evaluation revealed the implementation of AI-ECG resulted in a modest increase in short-term healthcare costs while ...
Artificial intelligence-enhanced electrocardiography for ...
AI-ECG outperforms physicians and clinicians in detecting arrhythmia. The DNN model detects a rhythm class prediction per second and provides unique and large ...
Artificial intelligence analysis of the single-lead ECG predicts ...
Results. A total of 1007 patients completed the two-year follow-up per protocol, of whom 339 (33.7%) had a positive AI-ECG (predicting LVEF ≤ ...
AI-enabled ECG algorithm performs well in the early ...
The AI-ECG algorithm demonstrated excellent performance metrics compared with echocardiography: sensitivity was 95.6%, specificity was 79.4% and ...
Economic analysis of an AI-enabled ECG alert system
The AI-ECG system was designed to detect high-risk mortality indicators from ECGs and immediately notify attending physicians through a warning ...
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clario.com
clario.com/about/newsroom/clarios-new-ai-powered-ecg-quality-score-tool-enhances-cardiac-safety-assessments/Clario's New AI-Powered ECG Quality Score Tool ...
Clario's ECG Quality Score tool uses AI and ML to reinforce cardiac safety data quality with actionable insight to sponsors and proactive remediation of data
Artificial intelligence for direct-to-physician reporting of ...
We conclude that the DeepRhythmAI model has excellent negative predictive value for critical arrhythmias, substantially reducing false-negative findings.
9.
medicine.yale.edu
medicine.yale.edu/news-article/new-ai-tool-identifies-risk-of-future-heart-failure/New AI Tool Identifies Risk of Future Heart Failure
Researchers developed an artificial intelligence (AI) tool that can identify individuals at high risk of developing heart failure using electrocardiogram (ECG) ...
PMcardio: AI-Powered ECG Interpretation | Powerful Medical
Measurable Results. See how PMcardio outperforms standard care in real clinical settings with up to 2× higher sensitivity in detecting heart attacks. Validated ...
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