Powerful Medical Receives €40 Million IPCEI Grant — read the full story

Get access

Publications

Abstracts
0
Clinical Studies
0
Clinical Investigations
0
Abstracts
0
Clinical Studies
0
Clinical Investigations
0
  • Case Report
  • Annals of Emergency Medicine
  • March 23, 2026

Chest Pain With Subtle But Lifesaving ECG Findings

This study presents a clinical case highlighting the diagnostic value of hyperacute T waves on electrocardiogram (ECG) for early detection of acute coronary occlusion. A 54-year-old woman presented with classic ischemic symptoms, yet her initial ECG was interpreted as normal by both automated analysis and clinician review. Closer inspection revealed subtle hyperacute T waves in the anterior leads, consistent with acute occlusion of the left anterior descending (LAD) artery. The case underscores how reliance on traditional ST-segment elevation criteria alone may delay recognition of life-threatening myocardial infarction.
  • Study
  • JACC Advances
  • March 25, 2026

AI-Enhanced Electrocardiogram for Detection of Occlusive Myocardial Infarction in High-Risk Non–ST-Segment Elevation Acute Coronary Syndrome

This study evaluates an AI-enhanced ECG model for detecting occlusive myocardial infarction (OMI) in patients with high-risk non–ST-segment elevation acute coronary syndrome, using angiography as the reference. The model improved rule-in accuracy with high specificity (78%) and reduced false-positive cath lab activations compared with standard care, while rule-out sensitivity remained limited on the initial ECG. Serial ECG analysis improved detection, supporting the use of AI as a triage aid alongside clinical judgment rather than a standalone diagnostic tool.
  • Study
  • Journal of Personalized Medicine
  • March 24, 2026

Detecting Occlusion Myocardial Infarction with an AI-Powered ECG Model: A Retrospective Cohort Study

This study investigates the clinical utility of personalized medicine approaches in a specific disease context, focusing on identifying relevant biomarkers, patient characteristics, and tailored management strategies. The authors highlight how integrating clinical, molecular, and patient-specific data can improve diagnosis, risk stratification, and treatment selection, while also addressing current limitations such as heterogeneity of evidence and challenges in implementation. Overall, the findings emphasize the growing role of precision medicine in optimizing outcomes and supporting more individualized, data-driven clinical decision-making.
  • Study
  • JACC Advances
  • March 23, 2026

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

This study evaluates the real-world performance of an AI-based ECG interpretation tool for detecting STEMI in patients undergoing emergent catheterization laboratory activation, using angiographic findings as the reference standard. Compared with standard clinical ECG interpretation, the AI model achieved higher sensitivity and specificity, significantly reducing false-positive activations while maintaining strong performance even in challenging cases such as arrhythmias or conduction abnormalities. These results suggest that AI-assisted ECG analysis can improve diagnostic accuracy, optimize triage, and support faster, more appropriate decision-making in acute coronary syndrome management.
  • Study
  • European Heart Journal – Digital Health
  • December 2, 2025

An AI Model for Electrocardiogram Detection of Occlusion Myocardial Infarction: A Retrospective Study to Reduce False Positive Cath Lab Activations

This single-centre retrospective study examined whether an AI-powered ECG model (PMcardio’s Queen of Hearts OMI model) can better detect acute occlusion myocardial infarction (OMI) and reduce false-positive cardiac catheterization laboratory (CCL) activations compared with traditional STEMI millimetre criteria. The authors analysed 304 consecutive STEMI pathway activations over a 2-year period at a tertiary academic centre and applied the AI model to pre-angiography 12-lead ECGs, comparing its performance against standard STEMI criteria. The AI model showed higher sensitivity (89.2% vs 68.3%), higher specificity (72.9% vs 51.7%), greater overall accuracy (82.9% vs 61.8%), and a high AUROC of 0.884 for identifying true OMI, while correctly flagging nearly three-quarters of false-positive activations as non-OMI. These findings suggest that integrating a specialized AI-ECG model into existing STEMI alert pathways could meaningfully reduce unnecessary CCL activations without compromising the detection of true occlusions.
  • Case Report
  • Cureus
  • November 26, 2025

Bifascicular Block Associated With Myocardial Infarction: A Marker of Proximal Left Anterior Descending Artery Occlusion Confirmed by the Artificial Intelligence-Based Smartphone App Queen of Hearts

This single-patient case report describes an elderly man presenting with chest pain, hypotension, and bifascicular block (BFB)—a combination of right bundle branch block (RBBB) and left anterior fascicular block (LAFB)—whose ECG showed QRS‑concordant anterior and lateral ST‑segment elevation consistent with a STEMI‑equivalent / occlusive myocardial infarction (OMI) pattern. Urgent coronary angiography revealed a long, severely calcified, near‑occlusive proximal left anterior descending (LAD) artery lesion, successfully treated with primary PCI and drug‑eluting stent implantation, achieving TIMI 3 flow. The Queen of Hearts (PMcardio) AI‑based smartphone app correctly classified the ECG as STEMI‑equivalent, identified atrial flutter and BFB, and predicted reduced left ventricular ejection fraction, later confirmed by echocardiography (EF 38%). This case underscores BFB with concordant anterior ST elevation as a high‑risk marker of proximal LAD‑culprit OMI and provides anecdotal evidence that specialized AI‑enabled ECG interpretation can support rapid, accurate decision‑making in ACS. 
  • Study
  • Resuscitation
  • November 19, 2025

Artificial Intelligence Versus Human Expertise: ECG-Based Detection of Occlusive Myocardial Infarction After Cardiac Arrest

This single-centre study tested whether AI-based ECG analysis can detect occlusive myocardial infarction (OMI) after cardiac arrest using post-ROSC ECGs from 97 patients with subsequent coronary angiography. A dedicated deep neural network (Queen of Hearts, QoH) achieved the highest discrimination for acute coronary occlusion (AUC 0.85) and OMI (AUC 0.75), outperforming human experts, with a more balanced trade-off between sensitivity and specificity. In contrast, two large language model–based chatbots (ChatGPT and a GPT-based EKG Analyst) showed near-perfect sensitivity but almost no specificity, labelling nearly all ECGs as OMI and thus providing no meaningful diagnostic discrimination. These findings suggest that specialized ECG-trained AI, such as QoH, may serve as a useful adjunct in post-resuscitation decision-making. In contrast, general-purpose LLMs are currently unsuitable for critical ECG diagnosis.
  • Study
  • Circulation: Cardiovascular Interventions
  • November 10, 2025

Association Between Artificial Intelligence Detected Features on the ECG and Presence of Microvascular Obstruction

This study tested whether artificial intelligence (AI)–extracted electrocardiogram (ECG) features can identify microvascular obstruction (MVO) after reperfusion in anterior ST‑segment elevation myocardial infarction (STEMI). Using pre‑ and post‑percutaneous coronary intervention (PCI) ECGs and a logistic‑regression model built on PMcardio (API v2.5), the AI approach predicted CMR‑confirmed MVO with an AUC of 0.83, 94% specificity, and 60% sensitivity; the figure reports ~76% overall accuracy. By emphasizing high specificity, the model aims to flag patients very likely to harbor MVO when cardiac MRI is impractical, enabling rapid risk stratification at the point of care.
  • Study
  • JACC Cardiovascular Interventions
  • October 28, 2025

AI-Enabled ECG Analysis Improves Diagnostic Accuracy and Reduces False STEMI Activations: A Multicenter U.S. Registry

In a large, multi-center evaluation presented as Late-Breaking Science at the TCT 2025 conference, investigators assessed the diagnostic accuracy of the Queen of Hearts™ AI algorithm for ST-segment elevation myocardial infarction (STEMI) detection in emergency care. The study compared AI-enhanced ECG interpretation against standard triage protocols across three U.S. PCI centers, encompassing more than 1,000 patients who activated emergent reperfusion pathways. Published in JACC: Cardiovascular Interventions, the results demonstrated significantly improved accuracy and reduced false activations when using AI-driven analysis.
  • Study
  • Journal of Electrocardiology
  • May 10, 2025

Electrocardiographic Diagnostic Possibilities for Atrial Fibrillation Using Artificial Intelligence: Differentiation from Sinus Rhythm and Other Arrhythmias with the Pmcardio App in Covid-19 Patients

This study evaluated how effectively artificial intelligence can detect atrial fibrillation (AF) in COVID-19 patients using ECG analysis. Researchers compared the PMcardio AI application’s performance with that of cardiologists and infectious disease specialists. Among 116 hospitalized COVID-19 patients, PMcardio achieved perfect diagnostic accuracy (sensitivity and specificity of 1.00) for AF detection, matching cardiologists and surpassing infectious disease specialists. The AI’s performance remained consistent across confidence levels, and its severity assessments correlated significantly with rhythm findings. These results highlight AI’s potential to improve arrhythmia detection, streamline care, and reduce unnecessary in-person evaluations during infectious disease outbreaks.
  • Study
  • The Journal of Clinical Medicine
  • September 18, 2025

Door-to-balloon Time Outperforms St-segment Elevation in Predicting the Stemi vs. Nstemi Final Diagnosis

In a two-center registry study, investigators compared traditional STEMI/NSTEMI classification with angiographic and interventional diagnoses. Although guideline-defined ST-elevation criteria underpin quality metrics and reperfusion standards, they often fail to capture acute coronary occlusion myocardial infarction (OMI). This discordance has led to proposals for an OMI/Non-OMI paradigm, grounded in pathophysiology rather than ECG morphology. The study examined whether door-to-balloon time < 120 min aligns more closely with final cardiologist adjudication than either ST-segment elevation on ECG or angiographic evidence of TIMI 0–1 flow.
  • Study
  • The Journal of the American College of Cardiology
  • August 31, 2025

Hyperacute T Waves Are Specific for Occlusion Myocardial Infarction, Even Without Diagnostic St Elevation

This study aimed to derive and validate a quantitative definition of hyperacute T waves (HATW) by developing the HATW score and evaluating its diagnostic accuracy for identifying acute coronary occlusion in patients with possible ACS but without STEMI criteria.
  • Study
  • Advances in Interventional Cardiology
  • May 19, 2025

Occlusion Myocardial Infarction: A Revolution in Acutecoronary Syndrome

The 2025 ACC Guideline for the Management of Patients With Acute Coronary Syndromes claims that “Patients with NSTEMI may have a partially occluded coronary artery leading to subendocardial ischemia, while those with STEMI typically have a completely occluded vessel leading to transmural myocardial ischemia and infarction.” This is accompanied by a visual representation of a partially occlusive thrombus labeled ‘NSTEMI’ above an electrocardiogram (ECG) showing ST depression and T wave inversion, and a completely occlusive thrombus labeled ‘STEMI’ above an ECG showing ST elevation. This paradigm has remained despite two decades of angiographic and evidence-based ECG advances, which highlight the multiple reasons why a revolution in acute coronary syndrome (ACS) is needed, and has begun.
  • Study
  • The American Journal of Emergency Medicine
  • July 30, 2025

Accuracy of cath lab activation decisions for STEMI-equivalent and mimic ECGs: Physicians vs. AI (PMcardio, queen of hearts)

This study aimed to measure physician accuracy for interpreting STEMI-equivalent and STEMI-mimic ECGs for catheterization laboratory activation (CLA) and compare their performance to a machine learning-based artificial intelligence algorithm, Queen of Hearts AI (QoH AI).
  • Study
  • European Heart Journal
  • July 9, 2025

Artificial intelligence-enhanced ECG detection of acute coronary occlusion in chest pain patients with ST-elevation in lead aVR – A direct comparison to conventional ECG criteria

In a single‑centre retrospective study of 145 consecutive emergency‑department patients whose first ECG showed ≥1 mm ST‑elevation in lead aVR, investigators compared conventional electrocardiographic rules with a deep‑learning classifier (PMcardio “Queen of Hearts”) for recognising an acute coronary occlusion (ACO). Angiography and biomarker adjudication proved ACO in 19 patients (13 %). At an optimised probability threshold, the AI system achieved an area‑under‑the‑ROC curve of 0.918, detected 63 % of occlusions, and—crucially—generated no false‑positive calls in the 54‑patient rule‑out subgroup. By contrast, classic STEMI criteria identified only one in four occlusions and would have prompted between four and fourteen unnecessary emergency catheterisations.
  • Study
  • Coronary Artery Disease
  • July 25, 2025

AI-enhanced recognition of occlusions in acute coronary syndrome (AERO-ACS): a retrospective study

In a one‑year, single‑centre cohort of 217 cath‑lab patients (72 STEMI, 145 NSTEMI), the AERO‑ACS study tested PMcardio’s AI ECG against traditional ST‑elevation rules for detecting angiographic occlusion‑MI. The algorithm matched STEMI sensitivity (86.5 % vs 83.3 %) while raising specificity to 82.2 % (vs 66.0 %), achieved 100 % sensitivity in STEMI cases, and flagged occlusions linked to a 12‑fold higher in‑hospital mortality risk—suggesting more accurate triage with fewer unnecessary activations.
  • Study
  • European Heart Journal
  • July 28, 2025

Failure of standard contemporary ST-elevation myocardial infarction electrocardiogram criteria to reliably identify acute occlusion of the left anterior descending coronary artery

ST-elevation (STE) criteria on the electrocardiogram (ECG) are poorly sensitive for acute coronary occlusion myocardial infarction (ACOMI or OMI). This study evaluates the sensitivity of STE criteria on serial ECGs for total left anterior descending (LAD) coronary artery occlusion. We compared STE criteria with expert interpretation and a validated artificial intelligence (AI) ECG model for diagnosing LAD OMI.
  • Abstract
  • Swedish Cardiovascular Spring Meeting, Malmö
  • June 23, 2025

Validation of an artificial intelligence ECG model for detection of acute coronary occlusion myocardial infarction in unselected emergency department chest pain patients

Across 24 513 consecutive chest-pain presentations to five Swedish emergency departments, the Queen of Hearts (QoH) AI-ECG model detected acute coronary occlusion myocardial infarction with 52 % sensitivity and 99 % specificity. It outperformed an extended STEMI algorithm applied to the same cohort (41 % sensitivity, 95 % specificity) and more than doubled the sensitivity of classic STEMI criteria (55 % versus 26 %) while preserving comparable specificity (99 % versus 98 %). This real-world validation demonstrates that QoH markedly improves early ECG recognition of OMI in unselected, non-traumatic chest-pain patients.
  • Abstract
  • German Society of Cardiology (DGK) Congress
  • April 24, 2025

Artificial Intelligence Based Detection of Acute Coronary Occlusion Compared to STEMI Criteria – External Validation Study in a Consecutive All-Comer German Chest Pain Unit Cohort

2nd place - DGK Young Investigator Awards 2025, Germany: In a real-world cohort of over 4,000 chest pain patients presenting to a German CPU, excluding obvious STEMI heart attacks diagnosed before arrival, an AI ECG model detected serious artery blockages (ACO) with 97.8% accuracy. It outperformed traditional STEMI criteria, achieving higher sensitivity (70.2% vs 28.8%) and fewer false positives.
  • Associated Research, Study
  • Journal of Electrocardiology
  • April 20, 2025

Precordial Swirl Sign: A New ECG Pattern of Left Anterior Descending Artery Occlusion Myocardial Infarction

This study identifies the novel “precordial swirl sign,” a distinct ECG pattern that can uncover critical LAD coronary artery blockages often missed by standard STEMI criteria. It offers a promising step toward earlier and more accurate detection of high-risk heart attacks.
  • Abstract
  • JACC Journals
  • April 1, 2025

First Objective Definition of Hyperacute T-waves as a ST-segment Elevation Myocardial Infarction Equivalent ECG Finding

This abstract introduces the first objective definition of hyperacute T-waves — an early and often missed ECG sign of a severe heart attack. By accurately detecting blocked arteries even before classic signs appear, the new formula helps doctors identify high-risk patients sooner and deliver life-saving treatment faster.
  • Study
  • Journal of Personalized Medicine
  • March 28, 2025

Artificial Intelligence Detection of Occlusive Myocardial Infarction from Electrocardiograms Interpreted as “Normal” by Conventional Algorithms

Conventional ECG algorithms, humanly programmed to detect abnormalities based on fiducial points, frequently miss critical patterns of STEMI. AI-driven deep neural network models like PMcardio AI ECG offer significant potential in identifying these dangerous false negatives, reducing the risk of false reassurance, and enhancing clinical decision-making.
  • Abstract, Study
  • Journal of Electrocardiology
  • February 19, 2025

High Precision ECG Digitization Using Artificial Intelligence

This study presents a fully automated AI solution for high-precision digitization of paper ECGs, including smartphone photos. It enables rapid ECG conversion in under 7 seconds, maintaining strong performance even in low-quality or distorted images. Ideal for use in ambulances, primary care, or low-resource settings, it bridges the gap to advanced AI interpretation and secure digital sharing when native digital ECGs are unavailable.
  • 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.
  • Case Report
  • Journal of Electrocardiology
  • January 10, 2025

The Crucial Role of Image Quality in AI-enabled ECG Digitization and Interpretation of Occlusion Myocardial Infarction

The effectiveness of AI-ECG tools depends on the quality of input data—poor ECG image quality can lead to critical misdiagnoses. This case shows how a low-quality scan triggered a false STEMI alert, later corrected with improved digitization. It underscores the importance of ensuring minimally acceptable image quality, as even advanced tools can’t fully correct severe distortions.
  • 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.
Female Patients with Occlusive Myocardial Infarction without ST Elevation Experience Longer Delays in Receiving Emergent Reperfusion
  • 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.
Deep-learning Assisted ECG-based Emergent Cathlab Activation
  • 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.
Artificial Intelligence Tool Accurately Predicts Occlusion Myocardial Infarction And May Reduce False-Positive Cath Lab Activations
  • 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.
From ST-Segment Elevation MI to Occlusion MI
  • 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
  • JACC Advances
  • 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.
International Evaluation of an Artificial Intelligence-powered ECG Model Detecting Acute Coronary Occlusion Myocardial Infarction
  • 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.
Load more

Stay on the pulse with our newsletter

Your submission was successful

Get updates, insights, and reports on the latest industry trends.

Try PMcardio free — scan your first ECG in under a minute.

Download PMcardio app on the Apple App Store
Get PMcardio app on Google Play Store
Download PMcardio app on the Apple App Store
Get PMcardio app on Google Play Store

Products

Company

Resources

Powerful Medical is redefining the landscape of medical diagnostics with its AI-driven solutions. The company’s PMcardio platform deploys certified AI-ECG Modules that revolutionize the way cardiovascular diseases are detected, offering rapid and accurate diagnostics that pave the way for timely and effective treatment.

Powerful Medical, Inc. | PMcardio and the PMcardio logo are registered trademarks of Powerful Medical. Additionally, Queen of Hearts is claimed as a trademark of Powerful Medical.

Certain AI ECG Modules are CE-marked medical devices under EU MDR and only certified for marketing in the European Union and the United Kingdom. Powerful Medical technology has not yet been cleared or approved 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.
Get access

Heart Attacks are #1 cause of death world-wide and killing about 12 milions people a year.

Clinical Definition of Problem

Contrary to popular belief, a heart attacks isn’t a blockage inside of the heart. A heart attack is a blockage of the coronary arteries supplying the heart muscle with oxygenated blood.

So let’s assume you get a blood clot here — it blocks the blood flow downstream, meaning the heart muscle doesn’t get oxygenated blood and heart tissue downstream starts to die.

Clinical Solution​

The way to fix it is relatively simple – doctors put in a stent that opens up the artery and renews blood flow. The latest clinical practice guidelines recommend that this “stenting” happens within 90 minutes from symptom onset.

If you don’t, even if you put in the stent in later, the heart tissue downstream has already been permanently damaged, which reduces the heart’s ability to pump blood. This is the leading cause of heart failure and increases 1-year mortality by two-fold.

Time is muscle.

You have just 90 minutes to diagnose the patient, bring them to the hospital and put in the stent, otherwise there is permanent damage. So problem is, that 1 in 2 heart attacks get initially misdiagnosed at the first point of contact.

Discover the future of medical work with us.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.