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Revolutionizing Cardiac Care: Cardio AI's Multi-Agent System Takes Center Stage at AHA Scientific Sessions 2025
The future of cardiovascular diagnostics arrived at the American Heart Association Scientific Sessions 2025 in New Orleans, where Cardio AI unveiled its groundbreaking Multi-Agent System—a unified intelligence platform poised to transform how we detect, diagnose, and manage heart disease.

A New Era in Cardiovascular Diagnostics
On November 8, 2025, Dr. Tamanna Nahar (Co-Founder & Chief Medical Adviser) and Sampson Kontomah (Founder & CEO) presented their revolutionary approach to cardiovascular care at the Basic Science Zone in New Orleans. Their presentation, "Development of a Multi-Agent System for Cardiovascular Diagnostic," showcased how artificial intelligence can seamlessly integrate multiple data sources to deliver comprehensive cardiac assessments in minutes rather than hours.

The Challenge: Fragmented Cardiac Care
Traditional cardiovascular diagnostic workflows are plagued by fragmentation. Cardiologists juggle data from ECGs, echocardiograms, laboratory values, electronic health records, and wearable devices—each existing in separate silos. This disjointed approach leads to delays in diagnosis, missed critical findings, and cognitive overload for physicians who must synthesize vast amounts of information under time pressure.

The Solution: Intelligent Integration Through Multi-Agent Architecture
Cardio AI's Multi-Agent System represents a paradigm shift in how we approach cardiac diagnostics. At its core is a "Master Orchestrator"—a central brain that coordinates three specialized subsystems:

1. Cardio AI Diagnostic Analytics
Seven specialized AI agents work in concert to analyze continuous ECG data, electrocardiographic patterns, laboratory values, and quantitative imaging. These agents don't just process data; they synthesize it into actionable clinical insights.

2. IoMT Real-Time Monitoring (Remote Patient Monitoring)
Continuous integration with Internet of Medical Things devices enables real-time vital sign tracking and automated alerts. The system processes wearable device data continuously, transforming reactive care into proactive intervention.

3. PACS/DICOM Imaging Analysis
Advanced imaging capabilities allow the platform to process and interpret medical images, integrating visual data seamlessly with other diagnostic modalities.

Breakthrough Technology: The AI Models Behind the System
Echocardiogram Analysis with EchoFrame
One of the presentation's highlights was EchoFrame, a lightweight yet highly accurate model for left ventricular ejection fraction (LVEF) estimation. Built on the 3D MobileNetV3 UNet architecture and trained on the EchoNet-Dynamic dataset containing 10,000 echocardiogram videos, EchoFrame achieves remarkable performance metrics:

Dice Coefficient: 0.8956
IoU (Jaccard Index): 0.9279
Precision: 0.9562
Recall: 0.9676
ROC AUC: 0.9968
The system performs LV segmentation and estimates ejection fraction with exceptional accuracy, identifying hyperdynamic LV function that requires clinical correlation. With an average LV volume of 36.39 mL and an ejection fraction of 35.9%, EchoFrame demonstrates its ability to support early detection of heart failure.

ECG Analysis: Inception1D Model
The Inception1D model, trained on the PTB-XL dataset with 21,837 annotated ECGs, delivers comprehensive cardiac rhythm analysis. This multi-scale temporal feature extraction model identifies:

Myocardial Infarction (MI): Including ASMI, IMI, and NSTMI
Conduction Defects (CD): LBBB, RBBB, and IVCD
Hypertrophy (HYP): LVH and RVH
ST-T Changes (STTC): ISC, DIG, and IST
With validation accuracy of 85.25% and 93% recall for MI detection, the model demonstrates exceptional sensitivity for detecting life-threatening conditions while maintaining high confidence in positive predictions (86.96% precision). The F1-score of 89.89% reflects balanced performance, and an ROC AUC of 89.28% confirms strong discriminative ability across all classes.

EHR Integration: Predictive Modeling
Three powerful machine learning models extract insights from electronic health records:

CAD MLP Model: Determines coronary artery disease presence using demographics, labs, ECG, echo, and symptoms (Dataset: Z-Alizadeh Sani, 303 patients)

Accuracy: 85.25%
F1-Score: 89.89%
Precision: 86.96%
Recall: 93.02%
Heart Disease MLP Model: Predicts general heart disease presence from demographic, clinical, and physiological data (UCI Cleveland Dataset)

Accuracy: 80.33%
F1-Score: 83.33%
Precision: 76.92%
Recall: 90.91%
EHR Disease Progression Model: Predicts patient progression to critical conditions using 30-day longitudinal data

Test Accuracy: 94.5%
F1-Score: 89.74%
Precision: 98.36%
Recall: 82.50%
These models demonstrate excellent recall, ensuring minimal missed cases while maintaining good generalization and consistent ROC AUC performance.

The Clinical Impact: From Symptom to Actionable Diagnosis in Minutes
The presentation included a compelling clinical use case that demonstrates the system's real-world potential:

Scenario: Patient with Chest Pain

IoMT wearable detects elevated heart rate and abnormal rhythm → Alert generated
Master Orchestrator routes to Cardio AI → ECG Agent analyzes 12-lead
High confidence STEMI detected (Tier 3) → Automatic cath lab notification
Echocardiography ordered → PACS subsystem processes, segments, quantifies, and deploys specialized agents
Cardio AI Diagnostic Agent synthesizes all data → Comprehensive assessment
Treatment Agent generates evidence-based protocol → ICD-10 coded report
Total time: 2-3 minutes from symptom to actionable diagnosis
A Three-Tier Confidence-Based Automation System
Cardio AI's intelligent automation system operates on three confidence levels, ensuring physician control while maximizing efficiency:

Tier 1 (< 80%): System suggests, cardiologist evaluates
Tier 2 (80-94%): Cardiologist confirms diagnosis, MD approves
Tier 3 (≥95%): Autonomous system execution with monitoring
This tiered approach preserves physician autonomy while enabling rapid response to critical conditions.

Benefits Across the Healthcare Ecosystem
For Clinicians
Cardio AI simplifies data interpretation, enhances diagnostic accuracy, and dramatically reduces cognitive load. By automating routine analysis and flagging critical findings, physicians can focus on clinical decision-making and patient care.

For Patients
Early detection and personalized, proactive healthcare translate into better outcomes. Patients benefit from rapid diagnosis, timely interventions, and continuous monitoring that catches problems before they become emergencies.

For Payors
Cost savings arise from reduced hospitalizations and improved resource allocation. The system supports value-based care models and enhances population health management by identifying high-risk patients early.

Key Innovation Highlights
The Cardio AI Multi-Agent System stands out through five critical innovations:

Unified Architecture: First integrated platform combining AI diagnostics, IoMT monitoring, and PACS imaging under a master orchestrator that enables cross-subsystem workflows and eliminates care silos
Comprehensive Data Integration: Continuous synthesis of wearable data, diagnostic studies, and medical imaging creates a complete patient view from screening through intervention
Seamless Clinical Workflows: Complete cardiac workup flows from IoMT vitals to ECG analysis to imaging to diagnosis to report, with automated task routing and sub-second to minutes processing time
Scalable Automation: Three-tier confidence-based system (Informative → Recommendation → Urgent → Critical) preserves physician autonomy while enabling rapid response and acknowledgment requirements ensure clinical oversight
Enterprise Capabilities: 14 total AI agents across three subsystems handle 12 distinct task types spanning the care continuum, with real-time event broadcasting, performance monitoring, and HIPAA-compliant comprehensive audit trails
The Road Ahead: A 2.5-Year Roadmap
Cardio AI's development pathway demonstrates a methodical approach aligned with FDA SaMD Clinical Decision Support Guidance (2023) and multiple regulatory frameworks including ISO 14971 Risk Management, HIPAA Privacy and Security Rules, and AHA/ACC/ESC Guidelines for MI, HF, CAD, heart disease classification, and remote patient monitoring.

The roadmap spans:

MVP Testing & Internal Validation
External Validation & Beta Testing
Clinical Trials & PMA Preparation
PMA Submission & Early Commercialization
Market Fit & Expansion
Currently in development, the Cardio AI Multi-Agent System has not yet been reviewed or approved by the U.S. Food and Drug Administration or other regulatory authorities—but the comprehensive validation strategy and regulatory alignment position it for successful clinical translation.

Conclusion: Heart Matters, Intelligence Amplifies
Cardio AI's presentation at AHA Scientific Sessions 2025 showcased more than just technology—it demonstrated a vision for the future of cardiovascular medicine. By synthesizing multimodal data through intelligent agents, the platform transforms fragmented diagnostic workflows into unified care pathways.

The promise is compelling: early detection leading to timely intervention, optimized healthcare resource allocation, reduced preventable cardiovascular deaths, and long-term disease management that keeps patients healthier longer.

As cardiovascular disease remains the leading cause of mortality globally, innovations like Cardio AI's Multi-Agent System represent not just technological advancement, but hope for millions of patients worldwide.

For more information about Cardio AI and their groundbreaking work, visit www.cardioailive.com or contact them at [email protected].

 
Presentation by Dr. Tamanna Nahar (MD, MBA, FACC, Co-Founder & Chief Medical Adviser) and Sampson Kontomah (Founder & CEO), Cardio AI

American Heart Association Scientific Sessions 2025 November 8, 2025 | 10:30 AM - 11:30 AM | New Orleans, LA Abstract ePoster Board Session