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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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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Updated: Oct 27, 2025

Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography
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Machine learning in optical coherence tomography angiography.

David Le1, Taeyoon Son1, Xincheng Yao1,2

  • 1Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA.

Experimental Biology and Medicine (Maywood, N.J.)
|July 19, 2021
PubMed
Summary
This summary is machine-generated.

Optical coherence tomography angiography (OCTA) provides high-resolution retinal imaging. Quantitative OCTA features and machine learning show promise for automated disease screening, reducing human error in diagnosing retinopathies.

Keywords:
Retinaartificial intelligenceconvolutional neural networkdeep learningmachine learningoptical coherence tomography angiographyretinopathy

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Optical coherence tomography angiography (OCTA) enables noninvasive, label-free imaging of retinal vasculature at capillary resolution.
  • Early-stage eye diseases often present with subtle microvascular changes undetectable by standard screening.
  • Manual analysis of OCTA images by clinicians is time-consuming, prone to error, and hinders large-scale screening.

Purpose of the Study:

  • To review recent advancements in quantitative OCTA features for objective retinal vascular assessment.
  • To explore the application of machine learning and deep learning for automated OCTA image analysis.
  • To discuss the potential of these technologies for classifying different retinopathies.

Main Methods:

  • Development and application of quantitative OCTA features for standardizing vascular measurements.
  • Utilizing machine learning algorithms for classification based on quantitative OCTA data.
  • Exploring deep learning models for automatic analysis and disease classification from OCTA images.

Main Results:

  • Quantitative OCTA features allow for standardized documentation of retinal vascular changes.
  • Machine learning models have demonstrated feasibility in classifying retinopathies using quantitative OCTA features.
  • Deep learning approaches are being explored for automatic OCTA image analysis and disease classification.

Conclusions:

  • Quantitative OCTA features offer a standardized approach to assessing retinal vasculature.
  • Machine learning and deep learning hold significant potential for automated, objective screening and classification of retinopathies.
  • These advancements could improve early detection and management of eye diseases.