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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...
164

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Related Experiment Video

Updated: Jun 18, 2025

Oxygenation-sensitive Cardiac MRI with Vasoactive Breathing Maneuvers for the Non-invasive Assessment of Coronary Microvascular Dysfunction
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Deep Learning in Cardiothoracic Ratio Calculation and Cardiomegaly Detection.

Jakub Kufel1, Iga Paszkiewicz2, Szymon Kocot3

  • 1Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Medyków 14, 40-752 Katowice, Poland.

Journal of Clinical Medicine
|July 27, 2024
PubMed
Summary

A new deep learning algorithm accurately calculates cardiothoracic ratio (CTR) on chest radiography (CXR), aiding in detecting cardiomegaly and pericardial effusion. This AI tool shows significant correlation with human measurements, offering potential for rapid clinical screening.

Keywords:
cardiomegalycardiothoracic ratiochest radiographconvolutional neural networksegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Cardiothoracic ratio (CTR) is a key indicator for assessing cardiomegaly and pericardial effusion on chest radiography (CXR).
  • Accurate CTR calculation is crucial for timely diagnosis and patient management.
  • Current methods may be time-consuming or prone to inter-observer variability.

Purpose of the Study:

  • To evaluate a deep learning algorithm's performance in calculating CTR from CXRs.
  • To assess the algorithm's utility in identifying cardiomegaly and pericardial effusion.
  • To compare AI-derived CTR measurements with human expert assessments.

Main Methods:

  • A deep learning model was trained on 1020 randomly selected CXR images.
  • The AI model segmented heart and lung anatomy to determine CTR.
  • Measurements were compared against those obtained using standard DICOM viewer software and manual annotations.

Main Results:

  • The AI model achieved high performance metrics, with Intersection over Union of 88.28% (training) and 83.06% (validation).
  • F1-scores were 90.22% (training) and 90.67% (validation).
  • AI-derived CTR, transverse thoracic diameter (TTD), and transverse cardiac diameter (TCD) showed statistically significant differences compared to human measurements (p < 0.001).

Conclusions:

  • The deep learning algorithm demonstrates a significant correlation with human measurements for CTR calculation.
  • The AI method shows potential as a screening tool or advisory support in clinical settings, particularly in time-sensitive environments like ICUs and ERs.
  • Further validation in clinical conditions is recommended before widespread adoption.