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Artificial Intelligence for the Interventional Cardiologist: Powering and Enabling OCT Image Interpretation.
Chandramohan, Nitin; Hinton, Jonathan; O'Kane, Peter; Johnson, Thomas W.
Afiliação
  • Chandramohan N; Translational Health Sciences, University of Bristol Bristol, UK.
  • Hinton J; University Hospitals Dorset NHS Foundation Trust Poole, UK.
  • O'Kane P; University Hospitals Dorset NHS Foundation Trust Poole, UK.
  • Johnson TW; Dorset Heart Centre, Royal Bournemouth Hospital Bournemouth, UK.
Interv Cardiol ; 19: e03, 2024.
Article em En | MEDLINE | ID: mdl-38532946
ABSTRACT
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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article