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Powerful Medical
27. November 2025
3 min to read

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

Overview

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 highlights 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 and accurate decision-making in ACS. 

Published in: Cureus
Published on: 26 November 2025

Background

Accurate recognition of occlusive myocardial infarction in the presence of conduction disturbances remains challenging. While current ESC guidelines acknowledge bundle branch blocks as important in ACS, they do not specifically define bifascicular block (RBBB + LAFB) as a STEMI‑equivalent, despite its association with proximal LAD or left main occlusion, extensive myocardial injury, heart failure, and increased mortality. In parallel, AI-powered ECG tools, such as Queen of Hearts, aim to go beyond simple ST-elevation thresholds to detect STEMI-equivalent/OMI patterns and estimate ventricular function, potentially reducing both missed OMI cases and unnecessary cath-lab activations.

Methods

This single‑case report describes a 77‑year‑old man with multiple cardiovascular comorbidities evaluated by emergency medical services for prolonged chest pain. Serial 12-lead ECGs were obtained and interpreted by clinicians, and then processed using the Queen of Hearts (PMcardio) smartphone app, which analyzed rhythm, conduction, ST-T patterns, and predicted left ventricular ejection fraction. The patient underwent emergent coronary angiography and primary PCI of the culprit lesion. The diagnostic work-up included laboratory testing (cardiac biomarkers, renal function, and lipid profile) and transthoracic echocardiography on the day after PCI to quantify wall-motion abnormalities and systolic function, followed by guideline-directed medical therapy and short-term clinical follow-up.

Results

The initial ECG (page 2) showed atrial flutter with 4:1 AV conduction, complete RBBB, LAFB, and QRS‑concordant ST‑segment elevation in V1–V4 with additional lateral ST elevation (I, aVL, V6) and reciprocal inferior changes, leading to a working diagnosis of anterolateral STEMI in the context of new or presumably new BFB. A second ECG (page 3) demonstrated further progression of anterior ST elevation. The Queen of Hearts app labeled the tracing as STEMI‑equivalent, detected the bifascicular block and atrial flutter, and predicted an EF <40%. Coronary angiography (page 4 and Video 1) revealed a severely calcified, near‑occlusive proximal LAD stenosis, treated successfully with drug‑eluting stent implantation and restoration of TIMI 3 flow; other coronary vessels showed only mild, non‑obstructive disease. Post‑PCI ECG (page 5) showed persistence of BFB but resolution of ST elevation with QRS‑discordant T‑wave changes typical for RBBB. Laboratory testing demonstrated markedly elevated high‑sensitivity troponin I and NT‑proBNP with moderate anemia and chronic kidney disease (Table 1, page 6). Echocardiography (Video 2) confirmed apical akinesia, anterior/anteroseptal hypokinesia, EF 38%, biatrial enlargement, and moderate ischemic mitral regurgitation, consistent with the AI‑predicted LV dysfunction. The in-hospital course was uncomplicated, and the patient was discharged after eight days on optimized heart failure and antithrombotic therapy.

Conclusion

In the appropriate clinical context, bifascicular block (RBBB + LAFB) with QRS‑concordant ST elevation in V1–V3 should be regarded as a STEMI‑equivalent pattern, strongly suggestive of proximal LAD‑culprit OMI and warranting immediate invasive evaluation and reperfusion. This case illustrates how the prompt recognition of this high-risk ECG presentation enabled timely PCI and how the Queen of Hearts AI application provided accurate, point-of-care confirmation of OMI and left ventricular dysfunction. Although AI‑based ECG tools appear promising adjuncts for real‑time ACS triage, they should complement rather than replace expert ECG interpretation and clinical judgment, and their broader performance must be validated beyond single‑case experience.

Authors: Lucio Giuseppe Granata, Francesco Russo, Simona Giubilato, Marilena Scarabelli, Francesco Amico

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Powerful Medical leads one of the most important shifts in modern medicine by augmenting human-made clinical decisions with artificial intelligence. Our primary focus is on cardiovascular diseases, the world’s leading cause of death.

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PMcardio is a CE-certified AI that reads ECGs and offers a complex assessment of 49 cardiac conditions. Clinically validated in 15+ studies and trusted by over 100,000 clinicians, it delivers rapid, expert‑level interpretations, empowering emergency physicians, GPs, nurses, paramedics, and cardiologists to act with confidence at the point of care. Available for Individuals and Organizations.

About Powerful Medical

Established in 2017, Powerful Medical has embarked on a mission to revolutionize the diagnosis and treatment of cardiovascular diseases. We are a medical company backed by 28 world-class cardiologists and led by our expert Scientific Board with decades of experience in daily patient care, clinical research, and medical devices. The results of our research are implemented, developed, certified, and brought to market by our 50+ strong interdisciplinary team of physicians, data scientists, AI experts, software engineers, regulatory specialists, and commercial teams.

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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. 

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.

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