Multiparametric cardiovascular magnetic resonance approach in diagnosing, monitoring, and prognostication of myocarditis

C Eichhorn, S Greulich, C Bucciarelli-Ducci… - Cardiovascular …, 2022 - jacc.org
Myocarditis represents the entity of an inflamed myocardium and is a diagnostic challenge
caused by its heterogeneous presentation. Contemporary noninvasive evaluation of patients …

[HTML][HTML] Cardiovascular magnetic resonance for evaluation of cardiac involvement in COVID-19: recommendations by the Society for Cardiovascular Magnetic …

VM Ferreira, S Plein, TC Wong, Q Tao… - Journal of …, 2023 - Elsevier
Abstract Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has
affected nearly 600 million people to date across the world. While COVID-19 is primarily a …

Toward replacing late gadolinium enhancement with artificial intelligence virtual native enhancement for gadolinium-free cardiovascular magnetic resonance tissue …

Q Zhang, MK Burrage, E Lukaschuk… - Circulation, 2021 - Am Heart Assoc
Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance
(CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but …

Artificial intelligence for contrast-free MRI: scar assessment in myocardial infarction using deep learning–based virtual native enhancement

Q Zhang, MK Burrage, M Shanmuganathan… - Circulation, 2022 - Am Heart Assoc
Background: Myocardial scars are assessed noninvasively using cardiovascular magnetic
resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast …

[HTML][HTML] Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

B Lambert, F Forbes, S Doyle, H Dehaene… - Artificial Intelligence in …, 2024 - Elsevier
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Present and future innovations in AI and cardiac MRI

MA Morales, WJ Manning, R Nezafat - Radiology, 2024 - pubs.rsna.org
Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular
diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and …

TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation

F Uslu, AA Bharath - Computers in Biology and Medicine, 2023 - Elsevier
Recently, deep networks have shown impressive performance for the segmentation of
cardiac Magnetic Resonance Imaging (MRI) images. However, their achievement is proving …

[HTML][HTML] Automatic uncertainty-based quality controlled T1 mapping and ECV analysis from native and post-contrast cardiac T1 mapping images using Bayesian vision …

TW Arega, S Bricq, F Legrand, A Jacquier… - Medical image …, 2023 - Elsevier
Deep learning-based methods for cardiac MR segmentation have achieved state-of-the-art
results. However, these methods can generate incorrect segmentation results which can …

Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

L Szabo, Z Raisi-Estabragh, A Salih… - Frontiers in …, 2022 - frontiersin.org
A growing number of artificial intelligence (AI)-based systems are being proposed and
developed in cardiology, driven by the increasing need to deal with the vast amount of …

Abc-based weighted voting deep ensemble learning model for multiple eye disease detection

K Uyar, M Yurdakul, Ş Taşdemir - Biomedical Signal Processing and …, 2024 - Elsevier
Background and objective The unique organ that provides vision is eye and there are
various disorders cause visual impairment. Therefore, the identification of eye diseases in …