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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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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Computed Tomography01:10

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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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Related Experiment Video

Updated: Aug 30, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Machine learning-based automatic estimation of cortical atrophy using brain computed tomography images.

Jae-Won Jang1,2,3, Jeonghun Kim4, Sang-Won Park2,3

  • 1Department of Neurology, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea.

Scientific Reports
|August 30, 2022
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Summary
This summary is machine-generated.

A new machine learning algorithm automatically estimates cortical atrophy from brain CT scans. This tool offers reliable and fast assessments, potentially aiding physicians in dementia diagnosis.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Dementia Diagnostics

Background:

  • Cortical atrophy is a key indicator of neurodegeneration, typically assessed using MRI.
  • Computed tomography (CT) is also used for dementia detection, but its role in atrophy assessment is less explored.
  • Existing methods for atrophy measurement are often subjective and time-consuming.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for automatic cortical atrophy estimation on brain CT scans.
  • To compare the performance of the developed algorithm against human expert ratings.
  • To assess the potential clinical utility of automated atrophy assessment in dementia diagnosis.

Main Methods:

  • A machine learning model combining convolutional neural networks and regularized logistic regression (RLR) was developed.
  • The model was trained using brain CT images from Alzheimer's dementia patients and cognitively normal subjects, with visual ratings from neurologists.
  • Algorithm performance was evaluated using accuracy, sensitivity, specificity, and area under the curve (AUC).

Main Results:

  • The RLR algorithm demonstrated high performance in estimating frontal atrophy (AUC 0.87), posterior atrophy (AUC 0.88), right medial temporal atrophy (AUC 0.88), and left medial temporal atrophy (AUC 0.90).
  • The automated system provided fast and reliable estimations, comparable to expert neurologists.
  • Feature importance analysis was conducted to understand the algorithm's decision-making process.

Conclusions:

  • The developed RLR-based algorithm offers a comprehensive and automated approach to rating cortical atrophy on brain CT.
  • This technology has the potential to significantly support physicians in clinical settings for early dementia detection and diagnosis.
  • Automated CT-based atrophy assessment could improve the efficiency and consistency of neurodegenerative disease evaluation.