98%
921
2 minutes
20
Intravascular optical coherence tomography (IVOCT) is a form of intra-coronary imaging that uses near-infrared light to generate high-resolution, cross-sectional, and 3D volumetric images of the vessel. Given its high spatial resolution, IVOCT is well-placed to characterise coronary plaques and aid with decision-making during percutaneous coronary intervention. IVOCT requires significant interpretation skills, which themselves require extensive education and training for effective utilisation, and this would appear to be the biggest barrier to its widespread adoption. Various artificial intelligence-based tools have been utilised in the most contemporary clinical IVOCT systems to facilitate better human interaction, interpretation and decision-making. The purpose of this article is to review the existing and future technological developments in IVOCT and demonstrate how they could aid the operator.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10964291 | PMC |
http://dx.doi.org/10.15420/icr.2023.13 | DOI Listing |
Rev Cardiovasc Med
July 2025
Department of Mechanical and Aerospace Engineering, Polito Med Lab, Politecnico di Torino, 10129 Torino, Italy.
Intravascular optical coherence tomography (IVOCT) is emerging as an effective imaging technique for accurately characterizing coronary atherosclerotic plaques. This technique provides detailed information on plaque morphology and composition, enabling the identification of high-risk features associated with coronary artery disease and adverse cardiovascular events. However, despite advancements in imaging technology and image assessment, the adoption of IVOCT in clinical practice remains limited.
View Article and Find Full Text PDFEur Heart J Digit Health
July 2025
Department of Medicine, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK.
Artificial intelligence (AI) tools hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear, which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and December 2024 describing AI-based diagnosis of CAD using IVOCT.
View Article and Find Full Text PDFIEEE Trans Med Imaging
June 2025
Coronary artery disease poses a significant global health challenge, often necessitating percutaneous coronary intervention (PCI) with stent implantation. Assessing stent apposition is crucial for preventing and identifying PCI complications leading to in-stent restenosis. Here we propose a novel three-dimensional (3D) distance-color-coded assessment (DccA) for PCI stent apposition via deep-learning-based 3D multi-object segmentation in intravascular optical coherence tomography (IV-OCT).
View Article and Find Full Text PDFJ Soc Cardiovasc Angiogr Interv
March 2025
Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas.
Background: Intravascular optical coherence tomography (IVOCT) adoption has been limited by the complexity of image interpretation. The interpretation of histologic subtypes beyond lipid, calcium, and fibrous is challenging to human readers. To assist and standardize IVOCT image analysis, we demonstrate an artificial intelligence algorithm based on a histology data set that identifies lipid pools, fibrofatty, calcified lipid, and calcified fibrous in human coronary arteries for the first time.
View Article and Find Full Text PDFiScience
April 2025
Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
The process of manually characterizing and quantifying coronary artery plaque tissue in intravascular optical coherence tomography (IVOCT) images is both time-consuming and subjective. We have developed a deep learning-based semantic segmentation model (EDA-UNet) designed specifically for characterizing and quantifying coronary artery plaque tissue in IVOCT images. IVOCT images from two centers were utilized as the internal dataset for model training and internal testing.
View Article and Find Full Text PDF