Deep Learning in Cardiology

Bizopoulos, Paschalis; Koutsouris, Dimitrios

doi:10.1109/rbme.2018.2885714

Cited by 130 publications

(67 citation statements)

References 252 publications

(346 reference statements)

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“…A new clinical prediction modeling algorithm has been developed to build heart failure survival prediction models and mostly applied to identify the complex patterns on EHR data with diverse predictor-response relationships [44]. In this context, a deep learning approach was efficiently implemented for cardiology applications [45], and risk analysis of cardiovascular disease using an auto-encoder algorithm [46]. Another work has suggested a Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) for heart disease diagnosis and has produced high sensitivity (98%) and high specificity (99%) for the KEGG Metabolic Reaction Network dataset [47].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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“…Existing solutions to these problems are to exclude the basal slice in CMR imaging or the use of a larger database, e.g., the UK Biobank [48], might help to enhance segmentation accuracy. Further studies should include the simultaneous cardiac function quantification and segmentation, and so on [49, 50].…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging

Yang

Zhang

Lei

et al. 2019

BioMed Research International

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Objectives. The purpose of this study was to segment the left ventricle (LV) blood pool, LV myocardium, and right ventricle (RV) blood pool of end-diastole and end-systole frames in free-breathing cardiac magnetic resonance (CMR) imaging. Automatic and accurate segmentation of cardiac structures could reduce the postprocessing time of cardiac function analysis. Method. We proposed a novel deep learning network using a residual block for the segmentation of the heart and a random data augmentation strategy to reduce the training time and the problem of overfitting. Automated cardiac diagnosis challenge (ACDC) data were used for training, and the free-breathing CMR data were used for validation and testing. Results. The average Dice was 0.919 (LV), 0.806 (myocardium), and 0.818 (RV). The average IoU was 0.860 (LV), 0.699 (myocardium), and 0.761 (RV). Conclusions. The proposed method may aid in the segmentation of cardiac images and improves the postprocessing efficiency of cardiac function analysis.

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“…Deep learning is currently the state-of-the-art method for medical image feature extraction and supervised analysis. Its superior performance has surpassed any other traditional machine learning algorithms in many applications, including cardiac imaging (48,49). This success is mainly attributed to the automatic generation of optimal features, rather than relying on handcrafted features.…”

Section: Deep Learning Network With Cardiac Shape Priorsmentioning

confidence: 99%

Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy

Gilbert

Mauger

Young

et al. 2020

Front. Cardiovasc. Med.

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In many cardiovascular pathologies, the shape and motion of the heart provide important clues to understanding the mechanisms of the disease and how it progresses over time. With the advent of large-scale cardiac data, statistical modeling of cardiac anatomy has become a powerful tool to provide automated, precise quantification of the status of patient-specific heart geometry with respect to reference populations. Powered by supervised or unsupervised machine learning algorithms, statistical cardiac shape analysis can be used to automatically identify and quantify the severity of heart diseases, to provide morphometric indices that are optimally associated with clinical factors, and to evaluate the likelihood of adverse outcomes. Recently, statistical cardiac atlases have been integrated with deep neural networks to enable anatomical consistency of cardiac segmentation, registration, and automated quality control. These combinations have already shown significant improvements in performance and avoid gross anatomical errors that could make the results unusable. This current trend is expected to grow in the near future. Here, we aim to provide a mini review highlighting recent advances in statistical atlasing of cardiac function in the context of artificial intelligence in cardiac imaging.

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Deep Learning in Cardiology

Cited by 130 publications

References 252 publications

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging

Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy

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