Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application
Year: 2021
Type: review
Abstract: Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.
Source: Annals of Translational Medicine
DOI: 10.21037/atm-20-7676
Authors: Luca Saba, Skandha S. Sanagala, Suneet K. Gupta, Vijaya K. Koppula, Amer M. Johri, Narendra N. Khanna, Sophie Mavrogeni, John R. Laird, Gyan Pareek, Martin Miner, Petros P. Sfikakis, Athanasios Protogerou, Durga P. Misra, Vikas Agarwal, Aditya M. Sharma, Vijay Viswanathan, Vijay S. Rathore, Monika Turk, Raghu Kolluri, Klaudija Viskovic, Elisa Cuadrado-Godia, George D. Kitas, Neeraj Sharma, Andrew Nicolaides, Jasjit S. Suri
Institutions: Azienda Ospedaliero-Universitaria Cagliari, Bennett University, Queen's University, Indraprastha Apollo Hospitals, Onassis Cardiac Surgery Center, St. Helena Hospital, Adventist Heart Institute, Brown University, Miriam Hospital, National and Kapodistrian University of Athens, Sanjay Gandhi Post Graduate Institute of Medical Sciences, University of Virginia, M.V. Hospital for Diabetes and Diabetes Research Centre, Kaiser Permanente, Kaiser Permanente Sacramento Medical Center, Institute for Advanced Study, OhioHealth, University Hospital for Infectious Diseases "Dr Fran Mihaljevic", Municipal Institute for Medical Research, Hospital del Mar Research Institute, Dudley Group NHS Foundation Trust, Indian Institute of Technology BHU, Banaras Hindu University, University of Nicosia
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W3158506830