Non-invasive Multimodality Imaging of Coronary Vulnerable Patient

Canu, Marjorie; Broisat, Alexis; Riou, Laurent; Vanzetto, Gérald; Fagret, Daniel; Ghezzi, Cathérine; Djaileb, Loïc; Barone-Rochette, Gilles

doi:10.3389/fcvm.2022.836473

Cited by 6 publications

(3 citation statements)

References 57 publications

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“…Typically, a cardiologist would refer a patient initially for a less-invasive cardiac imaging modality such as SPECT-MPI to assess for coronary artery disease. 9 , 39 The clinical and prognostic value of these cardiovascular imaging tests is constrained and challenged by the presence of significant intra and interobserver variability, suboptimal image quality, time-consuming process, operators’ fatigue, etc. 12 , 40 Some of these issues can be addressed by applying AI in cardiovascular imaging which has also prospered in recent years.…”

Section: Discussionmentioning

confidence: 99%

SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach

2023

Acta Med Philipp

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Background Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity and remains to be a top health priority in many countries. A non-invasive imaging modality for diagnosis of CAD such as single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) is usually requested by cardiologists as it displays radiotracer distribution in the heart reflecting myocardial perfusion. The interpretation of SPECT-MPI is done visually by a nuclear medicine physician and is largely dependent on his clinical experience and showing significant inter-observer variability. Objective The aim of the study is to apply a deep learning approach in the classification of SPECT-MPI for perfusion abnormalities using convolutional neural networks (CNN). Methods A publicly available anonymized SPECT-MPI from a machine learning repository ( https://www.kaggle.com/selcankaplan/spect-mpi ) was used in this study involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory approach of CNN hyperparameter selection to search for optimum neural network model was utilized with particular focus on various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), and number of dense nodes (32, 64, 128, 256). The base CNN model was also compared with the commonly used pre-trained CNNs in medical images such as VGG16, InceptionV3, DenseNet121 and ResNet50. All simulations experiments were performed in Kaggle using TensorFlow 2.6.0., Keras 2.6.0, and Python language 3.7.10. Results The best performing base CNN model with parameters consisting of 0.7 dropout, batch size 8, and 32 dense nodes generated the highest normalized Matthews Correlation Coefficient at 0.909 and obtained 93.75% accuracy, 96.00% sensitivity, 96.00% precision, and 96.00% F1-score. It also obtained higher classification performance as compared to the pre-trained architectures. Conclusions The results suggest that deep learning approaches through the use of CNN models can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. These CNN models can also be used as a dependable and valid second opinion that can aid physicians as a decision-support tool as well as serve as teaching or learning materials for the less-experienced physicians particularly those still in their training career. These highlights the clinical utility of deep learning approaches through CNN models in the practice of nuclear cardiology.

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Section: Discussionmentioning

confidence: 99%

SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach

2023

Acta Med Philipp

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“…MR spectroscopy (MRS) combines the spatial imaging obtained from MRI with spectral analysis to detect the chemical composition and metabolic state of cardiovascular tissue. MRS is able to detect a range of atoms, including 1-Hydrogen (1H), 31-Phosphorus (31P), and 13-Carbon (13C) and identifies holesteryl esters as the major class of lipids found in the lipid-rich necrotic core of vulnerable [ 35 ]. Non-invasive imaging is useful for the clinical decision making, risk stratification and establish the opportunity for intervention by a multidisciplinary team [ 27 ].…”

Section: Imaging Biomarkers Of Vulnerable Plaquesmentioning

confidence: 99%

Vulnerable Atherosclerotic Plaque: Is There a Molecular Signature?

Chiorescu

Mocan

Buda³

et al. 2022

IJMS

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Atherosclerosis and its clinical manifestations, coronary and cerebral artery diseases, are the most common cause of death worldwide. The main pathophysiological mechanism for these complications is the rupture of vulnerable atherosclerotic plaques and subsequent thrombosis. Pathological studies of the vulnerable lesions showed that more frequently, plaques rich in lipids and with a high level of inflammation, responsible for mild or moderate stenosis, are more prone to rupture, leading to acute events. Identifying the vulnerable plaques helps to stratify patients at risk of developing acute vascular events. Traditional imaging methods based on plaque appearance and size are not reliable in prediction the risk of rupture. Intravascular imaging is a novel technique able to identify vulnerable lesions, but it is invasive and an operator-dependent technique. This review aims to summarize the current data from literature regarding the main biomarkers involved in the attempt to diagnose vulnerable atherosclerotic lesions. These biomarkers could be the base for risk stratification and development of the new therapeutic drugs in the treatment of patients with vulnerable atherosclerotic plaques.

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“…The technology benefits from advanced radiofrequency analysis of reflected echo signals to generate multiple spectral parameters, using mathematical operations to convert the signals into color histograms for analyzing different plaque components ( 43 ). Using these techniques, VH-IVUS can then classify plaque into the four groups of fibrous plaque, fibrolipid plaque, necrotic core, and dense calcium, providing a morphological evaluation of lesion evolution ( 44 , 45 ). In a prospective multicenter registry (VICTORY) study, IVUS and IVUS-VH examinations during carotid artery interventional therapy were determined both feasible and safe, providing important insights into the qualitative and quantitative composition of carotid plaques ( 46 ).…”

Section: Imaging Identification Of Vulnerable Carotid Plaquementioning

confidence: 99%

Detecting vulnerable carotid plaque and its component characteristics: Progress in related imaging techniques

et al. 2022

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Carotid atherosclerotic plaque rupture and thrombosis are independent risk factors for acute ischemic cerebrovascular disease. Timely identification of vulnerable plaque can help prevent stroke and provide evidence for clinical treatment. Advanced invasive and non-invasive imaging modalities such as computed tomography, magnetic resonance imaging, intravascular ultrasound, optical coherence tomography, and near-infrared spectroscopy can be employed to image and classify carotid atherosclerotic plaques to provide clinically relevant predictors used for patient risk stratification. This study compares existing clinical imaging methods, and the advantages and limitations of different imaging techniques for identifying vulnerable carotid plaque are reviewed to effectively prevent and treat cerebrovascular diseases.

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Non-invasive Multimodality Imaging of Coronary Vulnerable Patient

Cited by 6 publications

References 57 publications

SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach

SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach

Vulnerable Atherosclerotic Plaque: Is There a Molecular Signature?

Detecting vulnerable carotid plaque and its component characteristics: Progress in related imaging techniques

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