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Related Concept Videos

Coronary Artery Disease I: Introduction01:30

Coronary Artery Disease I: Introduction

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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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Coronary Artery Disease II: Pathophysiology01:26

Coronary Artery Disease II: Pathophysiology

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Coronary Artery Disease (CAD) originates from a series of events that impair the function of coronary arteries, the blood vessels responsible for delivering oxygen-rich blood to the heart muscle. The pathophysiology of CAD is closely linked to atherosclerosis, a chronic inflammatory and lipid-driven condition affecting the vascular endothelium.1. Endothelial DamageThe process begins with damage to the vascular endothelium, which serves as a protective barrier between the blood and the vessel...
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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

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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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Acute Coronary Syndrome III: Diagnostic Studies01:30

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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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Updated: Aug 28, 2025

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
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Development and validation of a machine learned algorithm to IDENTIFY functionally significant coronary artery

Thomas Stuckey1, Frederick Meine2, Thomas McMinn3

  • 1Cone Health Heart and Vascular Center, Greensboro, NC, United States.

Frontiers in Cardiovascular Medicine
|September 19, 2022
PubMed
Summary

This study introduces a new machine learning algorithm for detecting coronary artery disease. The algorithm uses voltage and photoplethysmographic signals collected in an office setting. It does not require radiation or patient stress. The algorithm was validated against standard diagnostic tests. At certain thresholds, it matches the accuracy of these tests. The system offers advantages in accessibility and patient comfort. It may serve as a front-line diagnostic tool. The algorithm provides a non-invasive alternative to current diagnostic methods.

Keywords:
artificial intelligencecoronary artery diseasedigital healthfront line testingmachine learning (ML)coronary artery disease detectionnon-invasive diagnostic toolsmachine learning in cardiologyclinical validation of algorithms

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Area of Science:

  • Cardiovascular diagnostics within clinical medicine
  • Machine learning applications in medical imaging
  • Non-invasive diagnostic testing in cardiology

Background:

Current diagnostic tests for coronary artery disease vary widely in performance and require specialized equipment, radiation exposure, or patient stress. Standard methods like SPECT imaging involve high costs and logistical challenges. Prior research has shown limitations in accessibility and patient comfort. This gap motivated the development of a non-invasive alternative. No prior work had resolved the need for a radiation-free, stress-free test. The need for a simpler, office-based solution remains unmet. Existing methods lack portability and ease of use. This paper introduces a novel machine learning approach to address these limitations.

Purpose Of The Study:

The aim of the study was to develop and validate a machine learning algorithm for identifying functionally significant coronary artery disease. The algorithm is designed to operate in an office setting without requiring radiation or patient stress. The study focused on improving accessibility and reducing diagnostic barriers. The algorithm uses synchronized voltage gradient and photoplethysmographic signals. The goal was to match or exceed the performance of current diagnostic standards. The study aimed to validate the algorithm using clinical data. The algorithm was tested for sensitivity and specificity against gold-standard diagnostic methods. The purpose was to provide a front-line diagnostic tool with broader applicability.

Main Methods:

The study collected time-synchronized voltage gradient and photoplethysmographic signals from patients at rest. Signals were gathered for 230 seconds from subjects within seven days of diagnostic imaging. The data was paired with subject metadata and outcomes. A machine learning model was trained on a subset of 2,522 subjects. A cut point on the ROC curve was pre-specified for validation. An unseen test set of 965 subjects was used to evaluate the algorithm. Sensitivity and specificity were calculated at the selected cut point. The algorithm's performance was compared to existing diagnostic standards.

Main Results:

At the pre-specified cut point, the algorithm achieved a sensitivity of 0.73 (95% CI: 0.68-0.78). The specificity was 0.68 (95% CI: 0.62-0.74) at the same cut point. A different cut point yielded a negative predictive value of 0.99. At this point, sensitivity was 0.89 and specificity was 0.42. A cut point maximizing positive predictive value reached 0.12. This point had a sensitivity of 0.39 and specificity of 0.88. The algorithm's performance matched that of standard diagnostic tests. It offers advantages in non-invasiveness and patient comfort.

Conclusions:

The algorithm's performance is comparable to current diagnostic tests for coronary artery disease. It offers a non-invasive, radiation-free alternative to SPECT imaging. The system can be used in an office setting without patient stress. Multiple cut points allow customization of diagnostic thresholds. The negative predictive value matches that of coronary computed tomography angiography. The positive predictive value approaches that of myocardial perfusion imaging. The algorithm may serve as a front-line diagnostic tool. It provides advantages in accessibility and patient experience.

The algorithm uses synchronized voltage gradient and photoplethysmographic signals to detect coronary artery disease.

The test does not require radiation, expensive equipment, or induced patient stress.

The 230-second period was selected to capture sufficient physiological data at rest.

The ROC curve was used to determine optimal diagnostic thresholds for sensitivity and specificity.

At one cut point, the negative predictive value reached 0.99, matching coronary computed tomography angiography.

The authors propose that the algorithm may serve as a front-line diagnostic tool for coronary artery disease.