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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Interprofessional care for coronary artery disease includes pharmacological therapy and revascularization procedures.Pharmacological therapy for Coronary Artery Disease (CAD) aims to manage symptoms, prevent complications, and improve patient outcomes through various classes of medications:Antiplatelet Agents:Aspirin and Clopidogrel: These medications inhibit platelet aggregation, preventing blood clots, which is crucial for avoiding heart attacks and strokes. Doctors often prescribe these...
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Imaging Studies for Cardiovascular System V: CT01:28

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Coronary Artery Disease (CAD): An Overview with Scientific InsightsCoronary Artery Disease (CAD), often referred to as C-A-D, is a prevalent blood vessel disorder classified under the broader category of atherosclerosis. Atherosclerosis is a pathological process characterized by the hardening and narrowing of arteries due to the accumulation of atherosclerotic plaques. These plaques are composed of cholesterol, fatty substances, inflammatory cells, calcium, and fibrin, reducing blood flow to...
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Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram

Yin-Hao Lee1, Ming-Tsung Hsieh2, Chun-Chin Chang3

  • 1Division of Cardiology, Department of Medicine, Taipei City Hospital, Yang Ming Branch, Taipei, Taiwan; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Atherosclerosis
|August 22, 2023
PubMed
Summary

An artificial intelligence (AI) model using electrocardiograms (ECG) shows promise in identifying coronary artery disease (CAD). This AI tool performs comparably to traditional risk factors and surpasses cardiologists in diagnosing obstructive CAD.

Keywords:
Artificial intelligenceCoronary artery diseaseECG

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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Traditional coronary artery disease (CAD) risk assessment relies on symptoms, cardiovascular risk factors (CVRFs), and electrocardiograms (ECG).
  • Current ECG interpretation lacks established criteria for diagnosing CAD.
  • There is a need for improved diagnostic tools to identify patients with CAD.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI)-enabled ECG model for identifying patients with obstructive CAD.
  • To compare the AI model's performance against traditional CVRFs and cardiologists' interpretations.
  • To validate the AI model's efficacy on an external patient cohort.

Main Methods:

  • A cohort of 4951 patients undergoing coronary angiography (CAG) was analyzed.
  • Stacking models using deep learning and machine learning were developed utilizing age, gender, and ECG data.
  • Model performance was assessed by comparing its predictive accuracy (AUC, sensitivity, specificity, F1 score) against CVRFs and cardiologists, with external validation.

Main Results:

  • The AI model demonstrated comparable performance to CVRFs in predicting CAD (AUC 0.70 vs 0.71).
  • The AI model outperformed cardiologists in diagnosing obstructive CAD (F1 score 0.68 vs 0.41).
  • External validation confirmed generally consistent diagnostic findings, and combining ECG with CVRFs improved predictive accuracy (AUC 0.72).

Conclusions:

  • An AI-enabled ECG model can effectively assist in identifying patients with obstructive CAD.
  • The AI model's diagnostic performance is similar to traditional CVRF-based approaches.
  • This AI tool holds potential as a valuable clinical aid in outpatient settings for patient triage and further diagnostic testing.