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  • Associated Research, Study
  • Annals of Emergency Medicine
  • January 17, 2025

ECG Patterns of Occlusion Myocardial Infarction: a Narrative Review

This comprehensive review highlights the limitations of the traditional STEMI/NSTEMI classification for heart attacks and advocates for a more precise approach to diagnosis and patient triage. Instead of relying solely on standard ECG criteria, this method focuses on ECG patterns that more accurately reflect the severity of underlying coronary vessel disease. By identifying high-risk ECG changes beyond current STEMI guidelines, clinicians can detect heart attacks earlier, initiate treatment faster, and ultimately improve patient outcomes.
  • Abstract, Multicenter Clinical Investigation
  • JSCAI
  • January 13, 2025

Artificial Intelligence–Powered Electrocardiogram Detecting Culprit Vessel Blood Flow Abnormality: AI-ECG TIMI Study Design and Rationale

The AI-ECG TIMI study is a unique, multicenter registry currently enrolling patients to evaluate an AI-powered ECG model for detecting actively obstructed arteries in acute coronary syndrome (ACS). It is the first study to collect standard 12-lead ECGs precisely at the time of coronary angiography, providing novel insights into coronary occlusion and reperfusion. By identifying high-risk ECG patterns and assessing AI’s role in predicting intervention success, it paves the way for AI-driven precision cardiology in acute care.
  • Abstract
  • Circulation
  • November 11, 2024

AI-Powered Smartphone Application for Detection of Left Ventricular Systolic Dysfunction using 12-Lead ECG

Rapid screening methods for heart failure (leading cause of unplanned hospitalizations and healthcare costs) in patients without symptoms are limited. This study validated PMcardio LVEF AI ECG model in identifying reduced heart function on widely accessible 12-lead ECGs. In a study of over 100-thousand patients, PMcardio demonstrated high performance (AUC 0.963) in detecting patients with reduced heart function offering a fast, non-invasive and scalable screening tool.
  • Abstract
  • Circulation
  • November 11, 2024

Female Patients with Occlusive Myocardial Infarction without ST Elevation Experience Longer Delays in Receiving Emergent Reperfusion

Up to a third of high-risk heart attacks go unrecognized by traditional ECG diagnostic criteria, causing dangerous delays—an effect even more pronounced in women, who remain understudied in this context. This analysis reveals a 4.4-hour treatment delay for initially misclassified female patients compared to males, highlighting the urgent need for modern, unbiased diagnostic solutions.
  • Abstract
  • Circulation
  • November 11, 2024

Deep-learning Assisted ECG-based Emergent Cathlab Activation: First Prospective Implementation of a Smartphone-based System

In its first prospective performance evaluation, the PMcardio STEMI AI ECG Model outperformed standard ECG machine readings, detecting high-risk coronary blockage with 95.7% sensitivity vs. 47.8%. By correctly flagging 15 initially missed patients, the AI model demonstrated its potential to strengthen triage accuracy and accelerate life-saving decisions.
  • Abstract
  • Circulation
  • November 11, 2024

Artificial Intelligence Tool Accurately Predicts Occlusion Myocardial Infarction And May Reduce False-Positive Cath Lab Activations

A retrospective study at Washington University St. Louis evaluated the PMcardio STEMI AI ECG Model for optimizing heart attack triage in the ED. The model correctly identified all “true” cases and detected high-risk patients missed by the current ECG diagnostic framework. AI-flagged high-risk patients were more likely far more likely to receive appropriate management, supporting AI’s role in improving early ED decision-making.
  • Abstract
  • Circulation
  • November 11, 2024

AI-Enhanced Recognition of Occlusion in Acute Coronary Syndrome (AERO-ACS): A Retrospective Review

Conventional ECG criteria often fail to detect severe coronary blockages, leading to delayed treatment and worse outcomes. The team at Mt. Sinai Morningside performed a retrospective validation of the PMcardio STEMI AI ECG Model, demonstrating 81% sensitivity and 87% specificity in identifying high-risk patients. The AI model nearly doubled the sensitivity of STEMI criteria and correctly reclassified false positives, potentially reducing unnecessary catheterizations while ensuring no true heart attacks were missed.
  • Associated Research, Study
  • JACC Advances
  • November 3, 2024

State-of-the-Art Review – From ST-Segment Elevation MI to Occlusion MI: The New Paradigm Shift in Acute Myocardial Infarction

This state-of-the-art review explores the evolution of heart attack classification, challenging the limitations of the standard-of-care STEMI/NSTEMI framework. It advocates for a shift toward diagnosing heart attacks based on the presence of acute vessel occlusion rather than relying solely on standard ECG criteria. By redefining how myocardial infarctions are identified and managed, this approach has the potential to reduce misdiagnoses, optimize triage, and refine treatment prioritization in emergency cardiology.
  • Multicenter Clinical Investigation
  • clinicaltrials.gov
  • October 1, 2024

Time for a Diagnostic Paradigm Shift From STEMI/​NSTEMI to OMI/​NOMI (DIFOCCULT-3)

DIFOCCULT-3 is a randomized controlled study actively enrolling patients across 23 sites in Turkey. It evaluates AI-assisted ECG interpretation in detecting high-risk heart attack patterns. By comparing traditional STEMI/NSTEMI classification with an occlusion/non-occlusion model, the trial aims to improve acute coronary blockage detection. The primary endpoint includes patient mortality and re-hospitalization at 1-year follow-up, assessing its impact on long-term patient outcomes.
  • Abstract
  • JACC Journals
  • October 1, 2024

Evaluating AI Prediction of Occlusive Myocardial Infarction from 12-lead ECGs After Resuscitated Out-of-Hospital Cardiac Arrest

Rapid detection of coronary vessel blockage in out-of-hospital-cardiac-arrest patients is crucial, as timely treatment improves survival and neurological outcomes. Standard ECG criteria often miss critical markers, delaying treatment. This analysis showed that the PMcardio STEMI AI ECG Model could detect them with high accuracy (88.7% sensitivity, 81.4% specificity), showing its potential to speed up diagnosis and improve patient care.
  • Abstract
  • ACC Quality Summit
  • September 17, 2024

Performance of Artificial Intelligence Powered ECG Analysis in Suspected ST-Segment Elevation Myocardial Infarction

In one of the largest US regional STEMI care networks, the Midwest STEMI Consortium, the PMcardio STEMI AI Model accurately identified 89% of patients needing urgent management while reducing unnecessary catheterizations by 28%. AI-powered standardized ECG interpretation may optmize STEMI triage by reducing costly, unwarranted cath lab activations while ensuring precise and timely diagnosis.
  • Study
  • Pre-hospital Emergency Care
  • September 12, 2024

Artificial Intelligence Driven Prehospital ECG Interpretation for the Reduction of False Positive Emergent Cardiac Catheterization Lab Activations

Activating the cardiac catheterization lab too frequently can strain healthcare resources, yet overlooking an acute myocardial infarction carries significant risk. Evaluated in a prehospital setting by Hennepin Emergency Services (USA), the PMcardio STEMI AI ECG Model showed potential to optimize emergency cardiac care and improve resource efficiency by reducing false catheterization lab activations by 34% without missing any “true” heart attacks.
  • Study
  • Ukrainian Journal of Cardiovascular Surgery
  • June 27, 2024

Application of the Artificial Intelligence Model for Detection of Electrocardiographic Signs of Coronary Occlusion in Patients with Non ST-Elevation Acute Coronary Syndrome

Many heart attacks do not fit the textbook definition and occur without classic ST-elevation seen on ECG, complicating diagnosis. This single-center retrospective evaluation at the National Amosov Institute of Cardiovascular Surgery assessed the PMcardio STEMI AI ECG Model's ability to detect these subtle cases. The model achieved 85.3% accuracy, 67% sensitivity, and 93% specificity, highlighting its potential to help clinicians identify high-risk patients earlier, enabling timely and targeted care.
  • Abstract
  • Heart Rhythm
  • May 16, 2024

Single Center Retrospective Validation of an Artificial Intelligence ECG Model Detecting Acute Coronary Occlusion

In this single-center US-based cohort study of emergency department patients with suspected heart attacks, the PMcardio STEMI AI ECG Model outperformed standard STEMI criteria, improving detection sensitivity by 30%. Patients correctly identified by AI but missed by cardiologists faced notable treatment delays (~22 hours), underscoring the AI's potential to enable faster diagnosis and earlier intervention.
  • Abstract
  • German Society for Cardiology (DGK) Congress
  • April 10, 2024

Artificial intelligence-based detection of occlusion myocardial infarction: first external validation in a German chest-pain unit cohort

In a large-scale retrospective validation involving over 1,700 consecutive patients presenting to German Chest Pain Units, the PMcardio STEMI AI ECG Model demonstrated high diagnostic accuracy (95.3%) and specificity (96.7%) in identifying high-risk patients typically missed by standard ECG interpretation. This highlights its potential to significantly improve emergency care through earlier detection and intervention in a real-world, broad clinical setting.
  • Abstract, Study
  • Journal of Electrocardiology
  • December 23, 2023

Validation of an Automated Artificial Intelligence System for 12‑lead ECG Interpretation

Trained on over 1 million ECGs, the PMcardio Core AI ECG Model was evaluated across six diagnostic categories, outperforming primary care physicians by up to 73% and matching cardiologists in overall accuracy. Additionally, it demonstrated clear superiority over traditional ECG machine-generated diagnoses, which are limited by rule-based algorithms, restricted pattern recognition, and an inability to incorporate clinical context.
  • Study
  • European Heart Journal - DH
  • November 28, 2023

International Evaluation of an Artificial Intelligence-powered ECG Model Detecting Acute Coronary Occlusion Myocardial Infarction

As the leading publication in EHJ-Digital Health for the past year, this study presents the internal multi-centric validation of the PMcardio STEMI AI ECG Model for detecting acute coronary blockage. Trained on 18,616 ECGs, the model achieved an AUC of 0.938, with 80.6% sensitivity and 93.7% specificity. It significantly outperformed traditional STEMI criteria and matched expert interpretation, offering a groundbreaking advancement in emergent patient detection.
  • Abstract, Study
  • European Heart Journal
  • November 9, 2023

Poor Prognosis of Total Culprit Artery Occlusion in Patients Presenting with NSTEMI 

Traditional ECG criteria miss nearly one-third of heart attack patients with a fully occluded artery, mislabeling them as "NSTEMI" despite needing urgent care. This retrospective study of 10,000 patients found they face longer treatment delays and nearly double the one-year mortality of “acute” STEMI patients, highlighting the need for better ECG-based risk stratification and faster intervention.
  • Abstract
  • European Heart Journal
  • November 9, 2023

Diagnostic Accuracy of a Smartphone Application for Artificial Intelligence-based Interpretation of 12-lead ECG in Primary Care (AMSTELHEART-1)

Accurately interpreting 12-lead ECGs in primary care can be challenging for clinicians due to limited training, time constraints, and variability in expertise. This study validated the PMcardio Core AI ECG Model for AI-driven ECG analysis, demonstrating 86% sensitivity and 92% specificity for major abnormalities, along with near-perfect accuracy for atrial fibrillation, highlighting its potential to enhance early case detection.
  • Study
  • ESC Heart Failure
  • June 13, 2022

Utilizing Longitudinal Data in Assessing All-Cause Mortality in Patients Hospitalized with Heart Failure

In collaboration with the Cardiovascular Center Aalst in Belgium, Powerful Medical developed a machine learning algorithm to improve risk stratification in patients hospitalized with new or worsening heart failure. Trained on 2,449 patients and 151,451 exams, the model accurately predicts mortality across multiple time points (AUC-ROC 0.83–0.89), enabling proactive, yet personalized clinical interventions.
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PMcardio is CE-marked as class IIb medical device under EU MDR and only certified for marketing in the European Union and the United Kingdom. PMcardio technology has not yet been cleared by the US Food and Drug Administration (FDA) for marketing in the USA. Not all modules of the PMcardio platform may be available in your region.

Prior to use, reference the Instructions for Use, for more detailed information on Indications, Contraindications, Warnings, Precautions and Adverse Events.

PMcardio is CE-marked as class IIb medical device under EU MDR and only certified for marketing in the European Union and the United Kingdom. PMcardio technology has not yet been cleared by the US Food and Drug Administration (FDA) for marketing in the USA. Not all modules of the PMcardio platform may be available in your region. Prior to use, reference the Instructions for Use, for more detailed information on Indications, Contraindications, Warnings, Precautions and Adverse Events.

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