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Related Concept Videos

Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

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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Histology-Grounded Automated Plaque Subtype Segmentation in Intravascular Optical Coherence Tomography.

Paul Young1, Drew Nolen2, Thomas E Milner3

  • 1Department of Computer Science, University of Texas at San Antonio, San Antonio, Texas.

Journal of the Society for Cardiovascular Angiography & Interventions
|April 15, 2025
PubMed
Summary
This summary is machine-generated.

An artificial intelligence algorithm trained on histology data can now identify complex plaque subtypes in intravascular optical coherence tomography (IVOCT) images, improving diagnostic accuracy.

Keywords:
artificial intelligencehistologyintravascular optical coherence tomographymachine learningneural networksplaque classification

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Histopathology

Background:

  • Intravascular optical coherence tomography (IVOCT) interpretation is complex, limiting its clinical adoption.
  • Human readers struggle to identify histologic subtypes beyond basic classifications like lipid, calcium, and fibrous tissue.
  • Accurate plaque characterization is crucial for understanding cardiovascular disease progression.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) algorithm for automated identification of histologic plaque subtypes using IVOCT.
  • To improve the standardization and accuracy of IVOCT image analysis.
  • To assist clinicians in interpreting complex IVOCT findings.

Main Methods:

  • Sixty-seven human coronary arteries were imaged using IVOCT post-mortem and compared with detailed histologic examination.
  • IVOCT images were coregistered and segmented into specific histologic subtypes: lipid pools, fibrofatty tissue, calcified lipid, and calcified fibrous tissue.
  • A deep learning neural network was trained using expert-guided, histology-validated IVOCT segmentations.

Main Results:

  • The AI algorithm achieved validation and test Dice coefficients of 0.63 and 0.40 for combined lipid subtypes, respectively.
  • For combined calcium subtypes, the algorithm achieved validation and test Dice coefficients of 0.66 and 0.62, respectively.
  • The AI model demonstrated capability in identifying plaque subtypes not readily apparent to human interpretation.

Conclusions:

  • A histology-validated AI algorithm can accurately identify complex plaque subtypes from IVOCT images.
  • This AI tool offers a rapid and standardized solution for IVOCT image interpretation challenges.
  • The algorithm has the potential to enhance clinical decision-making in cardiovascular disease management.