Deep learning for cardiac computer-aided diagnosis: benefits, issues &amp; solutions

Loh, Brian Chung Shiong; Then, Patrick

doi:10.21037/mhealth.2017.09.01

Cited by 39 publications

(17 citation statements)

References 76 publications

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“…However, unhealthy or just disordered lifestyles nowadays certainly increase the risk of suffering heart diseases. The most important heart affliction, and the leading cause of death worldwide according World Health Organization [178], is the ischemic heart disease, which occurs when one or several coronary arteries are blocked, which causes nutrition loss of myocardial cells and result in a myocardial infarction or heart attack. The consequences of this affliction can be as serious as death, hence diagnosing and treating it as quickly as possible gives a better scope for survival and quality of life.…”

Section: Applications In Ecg Processingmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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Section: Applications In Ecg Processingmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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“…Moreover, AI may facilitate communication between physicians and patients by decreasing processing times and therefore increasing the quality of patient care. Loh et al [251] argue that deep learning and mobile technologies would expedite the proliferation of healthcare services to those in impoverished regions which in turn leads to further decline of disease rates. Mayer et al [237] state that big data promises to change cardiology through an increase in the data gathered but its impact goes beyond improving existing methods such as changing how insights are discovered.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Deep Learning in Cardiology

Bizopoulos

Koutsouris

2019

IEEE Rev. Biomed. Eng.

147

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The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

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“…In fact, for deep learning-based models, the more the input data increases, the more they become accurate and efficient. Deep learning plays a crucial role in successfully analyzing heart-related images [11]. The performance of the classification methods is enhanced by utilizing deep learning techniques.…”

Section: Figurementioning

confidence: 99%

CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering

Sharifrazi¹,

Alizadehsani²,

Joloudari³

et al. 2020

Preprint

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Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained as one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered as a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR is heavily dependent on the clinical presentation and non-specific features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose the Myocarditis. The hybrid CNN-KCL method performs the early and accurate diagnosis of Myocarditis. To the best-of-our-knowledge, a Convolutional neural network has never been used before for the diagnosis of Myocarditis. In this study, we used 47 subjects to diagnose myocarditis patients from Tehran's Omid Hospital. The total number of data examined is 10425. Our results demonstrate that CNN-KCL achieves 92.3% in terms of diagnosis myocarditis prediction accuracy which is significantly better than those reported in previous studies.

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Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

Cited by 39 publications

References 76 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning in Cardiology

CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering

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