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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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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Non-perfusion area (NPA) is a key biomarker for diabetic retinopathy (DR) ischemia.
  • Projection-resolved optical coherence tomographic angiography (PR-OCTA) visualizes retinal capillaries but current NPA algorithms struggle with poor scan quality.

Purpose of the Study:

  • To develop a robust non-perfusion area (NPA) detection algorithm for diabetic retinopathy (DR) using convolutional neural networks (CNN).
  • To improve the accuracy and reliability of NPA quantification in optical coherence tomographic angiography (OCTA) images.

Main Methods:

  • A novel convolutional neural network (CNN) algorithm was developed for NPA detection.
  • The CNN integrates information from OCT angiograms and OCT reflectance images.
  • The algorithm was validated across various signal strength indices and on healthy and DR eyes.

Main Results:

  • The CNN algorithm demonstrated high accuracy and repeatability in detecting non-perfusion areas (NPA).
  • The method effectively excluded signal reduction and motion artifacts.
  • Avascular features were detected accurately from local to global levels, preserving resolution.

Conclusions:

  • The proposed CNN-based algorithm offers a robust and accurate solution for NPA detection in PR-OCTA.
  • This advancement can enhance the reliable quantification of ischemia in diabetic retinopathy (DR).
  • The algorithm's performance across different scan qualities suggests clinical utility.