Arrhythmias

PanMB2-Net: a deep learning framework for preoperative MB2 canal risk assessment on panoramic radiographs.

OBJECTIVES: This study aimed to develop and validate a clinically motivated artificial intelligence framework for preoperative risk assessment of the second mesiobuccal (MB2) canal in maxillary first molars using panoramic radiographs. MATERIALS AND METHODS: A total of 388 panoramic radiographs were retrospectively collected. Stage 1 used YOLOv5 to localize maxillary first molars and crop tooth-le...

Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.

BACKGROUND: Pre-participation screening (PPS) in competitive athletes aims to identify cardiovascular diseases associated with sudden cardiac death (SCD). Although the 12‑lead electrocardiogram (ECG) represents the cornerstone of PPS, structural abnormalities may demonstrate limited or incomplete electrical expression, particularly in asymptomatic athletes with physiological remodeling. Artificial...

A novel method for EKG anomaly detection based on the double sliding window technique.

Traditional detection methods often rely on fixed thresholding or machine-learning models, which can be computationally expensive. This study introduc...

Differentiating HFmr/rEF from HFpEF using standard 12-lead ECG measurements: an interpretable machine learning study.

BACKGROUND: Differentiating heart failure (HF) with mildly reduced/reduced ejection fraction (HFmr/rEF) from HF with preserved ejection fraction (HFpE...

A covalently adaptive polymer welding strategy enabling integrated electronics for pre-hospital monitoring of myocardial infarction.

Myocardial infarction (MI) is a major contributor to cardiovascular diseases (CVDs), creating an urgent demand for wireless, real-time, and continuous...

Acute Aluminum Phosphide Poisoning: Evolving Perspectives on Toxic Mechanisms and Evidence-Based Clinical Management.

Aluminum phosphide (AlP) is a chemical compound that is used as a pesticide for suicidal purposes and can cause death, and it poses a challenge to hea...

Comparative Interpretation of Pediatric Electrocardiograms by Large Language Models and a Pediatrician.

OBJECTIVE: Large language models (LLMs) have been explored for clinical applications, yet their reliability in pediatric electrocardiogram (ECG) inter...

Non-invasive arterial blood pressure waveform generation in critically ill patients: A sensor-based deep learning approach.

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of t...

Automated analysis of wearable ECG: machine learning methods, preprocessing pipelines, and benchmark datasets.

Wearable and mobile electrocardiography (ECG) has rapidly expanded access to rhythm monitoring outside of clinical settings, but the single-or few-lea...

Precision-Driven Management of Great Saphenous Varicose Veins: A Narrative Review.

OBJECTIVES: Great saphenous vein (GSV) incompetence is common, but numerous treatment options complicate patient-treatment matching. This narrative re...

A Dual-Modal Wearable PPG Smartwatch with AI-Enhanced Correction for High-Accuracy and Continuous AF Burden Assessment.

Atrial fibrillation (AF) increases the risk of stroke and heart failure, yet accurate quantification of AF burden in daily life remains difficult. Alt...

Multimodal Machine Learning Model Predicting Postoperative Delirium Based on Heart Rate Variability: A Prospective Observational Study.

BACKGROUND: Postoperative delirium is a common and serious complication after general anesthesia; its accurate prediction remains a substantial challe...

Procedural Strategies for Optimal Transcatheter Aortic Valve Replacement: An International Position Statement.

The extension of transcatheter aortic valve replacement (TAVR) to younger patients with longer life expectancy has driven a shift in focus toward proc...

Impact of an AI algorithm for multi-day prediction of incident atrial fibrillation on clinical decision-making: PROVISION-AF study.

Atrial fibrillation (AF) is frequently asymptomatic and often remains undetected until complications arise. Although artificial intelligence (AI)-enab...

Artificial intelligence in cardiopulmonary resuscitation: a systematic review.

Cardiac arrest remains a major cause of mortality and neurological disability, and its management depends on rapid recognition, effective resuscitatio...

Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease.

Portable, scalable, and accessible artificial intelligence (AI)-enabled smartwatch technology shows promise as a cardiovascular risk stratification st...

A robust binary secretary bird optimization method for high-dimensional data classification.

Feature selection is a key step in machine learning-based decision systems, especially in medical and biomedical applications, where datasets often co...

Cerebellum-inspired memtransistors enable emergent differentiation for hardware-efficient novelty detection.

Artificial intelligence (AI) algorithms are currently executed using silicon-based hardware, resulting in excessively high energy demand for data cent...

Prediction model for recurrent laryngeal nerve injury in microwave ablation of thyroid nodules.

OBJECTIVE: This study aims to develop an explainable machine learning (ML) framework integrating clinical, imaging, and procedural features for predic...

A three-level risk stratification algorithm for arrhythmia based on CNN-LSTM palindromic structure.

Cardiovascular diseases are characterized by sudden onset, high mortality rates, and high recurrence rates, making early screening and timely interven...