Artificial intelligence and optical coherence tomography for the automatic characterisation of human atherosclerotic plaques.

ABSTRACT:

Background

Intravascular optical coherence tomography (IVOCT) enables detailed plaque characterisation in vivo, but visual assessment is time-consuming and subjective.

Aims

This study aimed to develop and validate an automatic framework for IVOCT plaque characterisation using artificial intelligence (AI).

Methods

IVOCT pullbacks from five international centres were analysed in a core lab, annotating basic plaque components, inflammatory markers and other structures. A deep convolutional network with encoding-decoding architecture and pseudo-3D input was developed and trained using hybrid loss. The proposed network was integrated into commercial software to be externally validated on additional IVOCT pullbacks from three international core labs, taking the consensus am

SUBMITTER: Chu M 

PROVIDER: S-EPMC9724931 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature