Chiao‐Hsiang Chang

Artificial Intelligence-Enabled Electrocardiography for Preoperatively Detecting Atrial Fibrillation and Mortality Risk in Patients with Sinus Rhythm

Chiao‐Chin Lee, Chin-Sheng Lin, Wen-Yu Lin, Chiao‐Hsiang Chang et autres

Background: Pre-existing atrial fibrillation (AF) and postoperative new-onset AF (NOAF) are independent perioperative risk factors associated with increased short-term mortality and adverse events.This study aimed to develop and validate an artificial intelligence (AI) model capable of detecting hidden AF, including both pre-existing …

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Neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio combination for acute coronary syndrome risk stratification: A retrospective observational study from a Metropolitan Medical Center in Taiwan

Yu-Cheng Chen, Wen-Yu Lin, Chin‐Sheng Lin, Chiao‐Hsiang Chang et autres

ObjectiveTo evaluate the prognostic value of the combined neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) for adverse outcomes in Taiwanese patients with acute coronary syndrome (ACS).MethodsThis retrospective, single-center, observational cohort study analyzed 653 ACS patients from the Tri-Service General Hospital-coronary artery disease …

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Universal Atrial Fibrillation Screening Using Electrocardiographic Artificial Intelligence: A Cost-Effective Approach in Rural Communities

Wei–Ting Liu, Chin‐Sheng Lin, Chin Lin, Tsung‐Kun Lin et autres

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Integrating Manual ECG Feature Extraction with Ensemble Learning for Myocardial Infarction Diagnosis

Wencheng Liu, Yu‐Lan Liu, Da‐Wei Chang, Chiao‐Chin Lee et autres

Abstract Background: Myocardial infarction (MI) is a leading cause of mortality worldwide. Electrocardiograms (ECGs) are primary diagnostic tools but face limitations with high-dimensional, imbalanced data. Combining manual ECG waveform feature extraction with machine learning may improve diagnostic accuracy. Aim: The aim of …

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Real-world application of deep learning for ECG-based prediction of coronary artery disease and revascularization needs

Chiao‐Hsiang Chang, Chin‐Sheng Lin, Chun‐Ho Lee, Chin Lin et autres

Abstract Aims Early detection of the need for coronary revascularization and timely intervention may reduce fatal events, but limited screening tools often leads to underdiagnosis. The aim of this study is to use a deep learning model (DLM) that utilizes electrocardiography (ECG) …

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Artificial Intelligence–Enabled ECGs for Atrial Fibrillation Identification and Enhanced Oral Anticoagulant Adoption: A Pragmatic Randomized Clinical Trial

Wei-Ting Liu, Chin Lin, Chiao‐Chin Lee, Chiao‐Hsiang Chang et autres

BACKGROUND: Atrial fibrillation (AF) is often underdiagnosed and undertreated by noncardiologists. This study evaluated whether artificial intelligence-enabled ECG (AI-ECG) alerts could improve AF diagnosis and non-vitamin K antagonist oral anticoagulant prescriptions by noncardiologists. METHODS: In this open-label, cluster randomized controlled trial (NCT05127460) …

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From electrocardiographic patterns to prognostic insights: AI-assisted perspectives on revascularization decisions

Chin Lin, Chiao‐Hsiang Chang, Chin‐Sheng Lin

ACS, acute coronary syndrome; AI, artificial intelligence; ECG, electrocardiogram; ED, Emergency Department; MI, myocardial infarction; NSTEMI, non-ST-elevation myocardial infarction; STEMI, ST-elevation myocardial infarction

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Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial

Dung‐Jang Tsai, Chin Lin, Wei–Ting Liu, Chiao‐Chin Lee et autres

BACKGROUND: Early diagnosis of low ejection fraction (EF) remains challenging despite being a treatable condition. This study aimed to evaluate the effectiveness of an electrocardiogram (ECG)-based artificial intelligence (AI)-assisted clinical decision support tool in improving the early diagnosis of low EF among …

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Iron Deficiency and the Risk of Incident Left Ventricular Dysfunction in Patients with Coronary Artery Disease: A Single-center Cohort Study

Chiao‐Hsiang Chang, Chiao‐Chin Lee, Shi-Chue Hsing, Hsin-Hui Chen et autres

Abstract Background: Heart failure (HF) is a complex and life-threatening condition that often coexists with comorbidities such as hypertension, type 2 diabetes mellitus, coronary artery disease (CAD), and iron deficiency (ID). However, the relationship between ID and the development of HF remains …

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Development of an Artificial Intelligence-Enabled Electrocardiography to Detect 23 Cardiac Arrhythmias and Predict Cardiovascular Outcomes

Wen-Yu Lin, Chin Lin, Wencheng Liu, Wei–Ting Liu et autres

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PO-FP-075 ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY FOR PREOPERATIVELY DETECTING ATRIAL FIBRILLATION AND MORTALITY RISK IN SINUS RHYTHM

Chiao‐Chin Lee, Chin‐Sheng Lin, Wen-Yu Lin, Chiao‐Hsiang Chang et autres

Artificial Intelligence–Powered Rapid Identification of ST-Elevation Myocardial Infarction via Electrocardiogram (ARISE) — A Pragmatic Randomized Controlled Trial

Chin Lin, Wei-Ting Liu, Chiao‐Hsiang Chang, Chiao‐Chin Lee et autres

BackgroundTimely diagnosis of ST-elevation myocardial infarction (STEMI) is crucial for the treatment of patients with acute coronary syndrome. Artificial intelligence–enabled electrocardiogram (AI-ECG) has shown potential for the accurate and timely detection of STEMI on 12-lead electrocardiograms (ECGs). However, its impact on clinical …

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