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Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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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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X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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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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Related Experiment Video

Updated: Jul 15, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays.

Zaid Mustafa1, Heba Nsour2

  • 1Department of Computer Information Systems, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt 19117, Jordan.

Diagnostics (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced machine learning algorithm for detecting lung anomalies in X-rays, improving diagnostic accuracy for conditions like tuberculosis and lung nodules.

Keywords:
CADabnormalitiescomputer vision techniquesdeep learning algorithmimage classificationimage processingimage techniquesmachine learningmagnetic resonance imagingobject detectionpneumonia

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Accurate and efficient diagnosis of lung conditions from chest X-rays is crucial for effective patient treatment.
  • Traditional diagnostic methods can sometimes miss subtle abnormalities, necessitating advanced analytical tools.

Purpose of the Study:

  • To develop and validate an advanced machine learning algorithm for the accurate detection of anomalies in chest X-ray images.
  • To provide healthcare professionals with a reliable tool for diagnosing diverse lung conditions, including respiratory infections, tuberculosis (TB), and lung nodules.

Main Methods:

  • Utilized a vast collection of chest X-ray images for analysis.
  • Employed deep learning (DL) algorithms, object recognition, and categorization models, specifically adapting the You Only Look Once (YOLO) v8 algorithm.
  • Implemented data augmentation techniques such as scaling, rotation, and imitation to enhance training dataset diversity.

Main Results:

  • The modified YOLO v8 algorithm effectively classified X-ray images into distinct categories of lung abnormalities.
  • The algorithm demonstrated high accuracy in identifying unique and critical findings often missed by conventional diagnostic methods.
  • Achieved reliable detection of anomalies indicative of respiratory infections, tuberculosis (TB), and lung nodules.

Conclusions:

  • Machine learning (ML) algorithms, particularly advanced DL models like YOLO v8, offer a reliable and efficient tool for diagnosing lung disorders.
  • The developed algorithm enhances diagnostic capabilities, enabling healthcare practitioners to achieve greater accuracy and efficiency in identifying lung conditions.