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Related Concept Videos

Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Related Experiment Video

Updated: May 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Optimising coronary imaging decisions with machine learning: an external validation study.

L Malin Overmars1, Bram van Es2, Floor Groepenhoff3

  • 1Central Diagnostic Laboratory, University Medical Centre Utrecht, Utrecht, The Netherlands l.m.overmars-2@umcutrecht.nl.

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|April 25, 2025
PubMed
Summary

Sex-stratified machine learning algorithms using electronic health records (EHRs) show high negative predictive values for excluding coronary stenosis. While promising, further refinement is needed before widespread clinical use.

Keywords:
Angina PectorisChest PainCoronary StenosisDiagnostic ImagingElectronic Health Records

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Diagnosing coronary stenosis is challenging and current methods like CT and angiography are costly and invasive.
  • Electronic health records (EHRs) offer a potential non-invasive alternative for excluding coronary stenosis.
  • External validation of sex-stratified algorithms is crucial for assessing generalizability across different healthcare settings.

Purpose of the Study:

  • To externally validate sex-stratified machine learning algorithms for predicting the absence of coronary stenosis.
  • To evaluate algorithm performance in diverse clinical settings using EHR data.

Main Methods:

  • Sex-stratified XGBoost algorithms were developed using EHR data from 14,674 patients.
  • Algorithms were externally tested on EHR data from 9,252 patients across 13 cardiology centers.
  • Absence of coronary stenosis was determined via text mining of radiology reports; performance was measured by negative predictive values (NPVs) and specificities.

Main Results:

  • In the training cohort, algorithms achieved NPVs of 0.95 (men) and 0.93 (women) with specificities of 0.14 (men) and 0.26 (women).
  • In the testing cohort, NPVs were 0.89 (men) and 0.87 (women), with specificities of 0.07 (men) and 0.18 (women).
  • High NPVs were observed across different settings, indicating strong predictive power for the absence of stenosis.

Conclusions:

  • Sex-stratified machine learning algorithms using EHR data can non-invasively predict the absence of coronary stenosis with high NPVs.
  • The modest specificity suggests limitations for immediate clinical adoption.
  • Further research and refinement are necessary before these algorithms can be widely implemented in clinical practice.