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Validating Artificial Intelligence-Assisted Classification of Peripheral Arterial Disease Lesion Composition From 9.4 Tesla Magnetic Resonance Imaging With Histology

Year: 2024

Type: article

Abstract: MRI is a promising tool for identifying tissue properties and enhancing the precision of endovascular peripheral artery disease (PAD) treatment. However, the interpretation of images might pose a challenge for untrained experts. Unsupervised artificial intelligence (AI) algorithms can assist in interpreting MRI images and classifying lesion components. The aim of this study was to confirm the feasibility of a custom-made variational autoencoder (VAE) in evaluating lesion crossability through histological validation.

Source: JVS Vascular Science

DOI: 10.1016/j.jvssci.2024.100256

Authors: Judit Csore, Christof Karmonik, Bright Benfor, Peter Osztrogonacz, Trisha Roy

Language: English

Topic: Peripheral Artery Disease Management

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,800

OpenAlex ID: W4403779083

FWCI:

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