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Related Concept Videos

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.
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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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 I: CT and MRI01:14

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external

Ali Abbasian Ardakani1, Robert M Kwee2, Mohammad Mirza-Aghazadeh-Attari3

  • 1Radiology Technology Department, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Pattern Recognition Letters
|September 28, 2021
PubMed
Summary
This summary is machine-generated.

This study validated the COVIDiag artificial intelligence (AI) system for diagnosing COVID-19 pneumonia using computed tomography scans. The AI demonstrated accurate and consistent performance across multiple international databases, showing its real-world applicability.

Keywords:
Artificial intelligenceCoronavirus infectionsMachine learningPneumoniaTomography, X-ray computed

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Infectious Disease Diagnostics

Background:

  • Computed tomography (CT) is crucial for diagnosing COVID-19 pneumonia, but increasing patient numbers strain radiology departments.
  • Artificial intelligence (AI) offers a solution to improve diagnostic efficiency and quality.
  • Limited real-world validation hinders AI adoption in clinical settings.

Purpose of the Study:

  • To validate the clinical utility and diagnostic performance of the COVIDiag AI system for COVID-19 pneumonia.
  • To assess the generalizability and objectivity of the AI model across diverse multinational patient cohorts.
  • To provide real-world evidence for the application of AI in diagnosing COVID-19.

Main Methods:

  • A clinical AI system, COVIDiag, was evaluated using CT scans from 50 COVID-19 and 50 non-COVID-19 pneumonia cases across five international centers.
  • Performance metrics including sensitivity, specificity, accuracy, and Area Under the ROC Curve (AUC) were computed for each database.
  • The AI model's diagnostic performance was analyzed across multinational datasets.

Main Results:

  • The COVIDiag model demonstrated high diagnostic performance across all cohorts, with an overall AUC of 0.921 (sensitivity 88.8%, specificity 87.0%, accuracy 88.0%).
  • The system achieved excellent sensitivity (up to 92.0% in the Italian cohort) and AUC values ranging from 0.882 to 0.983 across different databases.
  • Bilateral upper and lower lobe involvement with ground-glass opacities was the most common CT finding in COVID-19 cases.

Conclusions:

  • The COVIDiag AI system accurately diagnoses COVID-19 pneumonia, confirming its efficacy.
  • The AI model exhibits consistent, optimal diagnostic performance on multinational databases, indicating strong generalizability.
  • This study provides significant real-world evidence supporting the clinical applicability of AI in diagnosing infectious respiratory diseases.