scholarly journals Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Luis Eduardo Juarez-Orozco ◽  
Octavio Martinez-Manzanera ◽  
Andrea Ennio Storti ◽  
Juhani Knuuti
Keyword(s):  
Machine Learning ◽  
Myocardial Ischemia ◽  
Nuclear Cardiology

scholarly journals Machine learning for nuclear cardiology: The way forward

2018 ◽  
Vol 26 (5) ◽  
pp. 1755-1758 ◽  
Author(s):  
Sirish Shrestha ◽  
Partho P. Sengupta
Keyword(s):  
Machine Learning ◽  
Nuclear Cardiology ◽  
The Way

scholarly journals Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

2021 ◽  
Author(s):  
Riemer H. J. A. Slart ◽  
Michelle C. Williams ◽  
Luis Eduardo Juarez-Orozco ◽  
Christoph Rischpler ◽  
Marc R. Dweck ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computed Tomography ◽  
Clinical Practice ◽  
Nuclear Cardiology ◽  
European Association ◽  
Imaging Techniques ◽  
Cardiovascular Imaging ◽  
Position Paper ◽  
Daily Clinical Practice

AbstractIn daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

scholarly journals Combined Metabolomics and Machine Learning Algorithms to Explore Metabolic Biomarkers for Diagnosis of Acute Myocardial Ischemia

2021 ◽  
Author(s):  
Jie Cao ◽  
Jian Li ◽  
Zhen Gu ◽  
Jia-jia Niu ◽  
Guo-shuai An ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Ischemia ◽  
Multilayer Perceptron ◽  
Learning Algorithms ◽  
Acute Myocardial Ischemia ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Post Mortem ◽  
Blood Samples ◽  
Mlp Model

Abstract Background: Acute myocardial ischemia (AMI) remains the leading cause of death worldwide. In particular, when death occurs within a short time, it is hard to find post-mortem specific structural anomalies of the heart at autopsy with standard methods. Therefore, the post-mortem diagnosis of AMI represents a current challenge for both clinical and forensic pathologists. Metabolomics technology plays an important role in searching for new diagnostic biomarkers. Here, we characterize metabolic profiles of AMI and attempted to interpret the role of metabolic changes in sudden cardiac death (SCD).Methods: The untargeted metabolomics was applied to analyze serum metabolic signatures from AMI experimental group (ligation of left coronary artery at 5mm below the left atrial appendage in rats), along with the control and sham groups (n = 10 per group). The analytical strategy based on ultra performance liquid chromatography combined with high-resolution mass spectrometry. The resulting data was preprocessed to discriminant metabolites, and a set of machine learning algorithms were used to construct predictable models. Seventeen blood samples from autopsy cases were applied to validate the classification model's value in human samples.Results: A total of 28 endogenous metabolites in serum were significantly altered in AMI group relative to control and sham groups. Gradient tree boosting, support vector machines, random forests, logistic regression, and multilayer perceptron models were used to further screen the more valuable metabolites from 28 metabolites to optimize the biomarker panel. The results showed that classification accuracy and performance of multilayer perceptron (MLP) models were better than other algorithms when the metabolites consisting of L-threonic acid, N-acetyl-L-cysteine, CMPF, glycocholic acid, L-tyrosine, cholic acid, and glycoursodeoxycholic acid. In autopsy cases, the MLP model constructed based on rat dataset achieved an accuracy of 88.23, and ROC of 0.89 for predicting AMI-SCD.Conclusions: A panel of 7 molecular biomarkers was identified by assessment the accuracy and efficacy of different metabolite combinations in inferring AMI using machine learning algorithms. The constructed MLP model has a high diagnostic performance for both AMI rats and autopsies-based blood samples. Thus, the combination of metabolomics and machine learning algorithms provides a novel strategy for AMI diagnosis.

scholarly journals Machine learning in the integration of simple variables for identifying patients with myocardial ischemia

2018 ◽  
Vol 27 (1) ◽  
pp. 147-155 ◽  
Author(s):  
Luis Eduardo Juarez-Orozco ◽  
Remco J.J. Knol ◽  
Carlos A. Sanchez-Catasus ◽  
Octavio Martinez-Manzanera ◽  
Friso M. van der Zant ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Ischemia

scholarly journals Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation

PLoS Medicine ◽  
2018 ◽  
Vol 15 (11) ◽  
pp. e1002693 ◽  
Author(s):  
Hyeonyong Hae ◽  
Soo-Jin Kang ◽  
Won-Jang Kim ◽  
So-Yeon Choi ◽  
June-Goo Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Ischemia ◽  
Learning Assessment

scholarly journals A Bibliometric Analysis of 14,822 Researches on Myocardial Reperfusion Injury by Machine Learning

2021 ◽  
Vol 18 (15) ◽  
pp. 8231
Author(s):  
Chan Li ◽  
Zhaoya Liu ◽  
Ruizheng Shi
Keyword(s):  
Machine Learning ◽  
Myocardial Ischemia ◽  
Reperfusion Injury ◽  
Latent Dirichlet Allocation ◽  
Time Factors ◽  
Mesh Term ◽  
Myocardial Reperfusion ◽  
Myocardial Reperfusion Injury ◽  
Medical Subject Headings ◽  
The Past

Myocardial ischemia is the major cause of death worldwide, and reperfusion is the standard intervention for myocardial ischemia. However, reperfusion may cause additional damage, known as myocardial reperfusion injury, for which there is still no effective therapy. This study aims to analyze the landscape of researches concerning myocardial reperfusion injury over the past three decades by machine learning. PubMed was searched for publications from 1990 to 2020 indexed under the Medical Subject Headings (MeSH) term “myocardial reperfusion injury” on 13 April 2021. MeSH analysis and Latent Dirichlet allocation (LDA) analyses were applied to reveal research hotspots. In total, 14,822 publications were collected and analyzed in this study. MeSH analyses revealed that time factors and apoptosis were the leading terms of the pathogenesis and treatment of myocardial reperfusion injury, respectively. In LDA analyses, research topics were classified into three clusters. Complex correlations were observed between topics of different clusters, and the prognosis is the most concerned field of the researchers. In conclusion, the number of publications on myocardial reperfusion injury increases during the past three decades, which mainly focused on prognosis, mechanism, and treatment. Prognosis is the most concerned field, whereas studies on mechanism and treatment are relatively lacking.

Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram

2020 ◽  
Author(s):  
Zhen-Yu Shu ◽  
Si-Jia Cui ◽  
Yue-Qiao Zhang ◽  
Yu-Yun Xu ◽  
Shng-Che Hung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Ischemia ◽  
Chronic Myocardial Ischemia
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