Artificial Intelligence versus Doctors’ Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography

Ponomariov, Victor; Chirila, Liviu; Apipie, Florentina-Mihaela; Abate, Raffaele; Rusu, Mihaela; Wu, Zhuojun; Liehn, Elisa A.; Bucur, Ilie

doi:10.15190/d.2017.6

Cited by 9 publications

(5 citation statements)

References 47 publications

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“…Moreover, these technologies are also frequently used in monitoring activities that would affect the health status of an individual, like measuring heart rate during physical activities and monitoring sugar level and blood pressure readings [15], [16]. Hence, a trustworthy intelligent system should be able to showcase impressive findings that are related to health based on an individual's daily routine behavior [17], [18]. However, existing studies indicated that that need to be resolved for further improvements and these issues represent the loose holes that present in the analytics of life diseases through technology-oriented solutions [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Methodical Evaluation of Healthcare Intelligence for Human Life Disease Detection

Abdul Ghani,

Iqbal,

Hamid

et al. 2023

MJCS

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Event intelligence for early diseases detection is highly demanded in current era and it requires reliable technology-oriented applications. Trusted emerging technologies play a vital role in modern healthcare systems for early diagnoses of different medical conditions because it helps to speed up the treatment process. Despite the enhancement of current healthcare systems, robust diagnosis of different type of diseases for intra-patients (outside of hospital settings) is still considered as a difficult task. However, the continuous evolution of trusted technologies in health sectors narrate the reboot process which could upgrades the healthcare service provision as the trusted next generation health units. In order to assist the healthcare providers to carry out early diseases’ detection for intra-patient clients, we designed this systematic review. We extracted 40 studies from the databases i.e. IEEE Xplore, Springer, Science direct and Scopus, from March 2016 and February 2021, and we formulated our research questions based on these studies. Subsequently, we rectified these studies using two filtration schemes namely, inclusion-omission policy and quality assessment, and as a result, we obtained 19 studies which successfully mapped our defined research questions .We found that these 19 studies clearly highlighted the different trusted architecture of internet of things, mobile cloud computing and machine learning, that are significantly beneficial to diagnose medical conditions for the intra-patient clients such as neurological diseases, cardiac malfunctions and other common diseases.

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Section: Introductionmentioning

confidence: 99%

Methodical Evaluation of Healthcare Intelligence for Human Life Disease Detection

Abdul Ghani,

Iqbal,

Hamid

et al. 2023

MJCS

View full text Add to dashboard Cite

show abstract

“…Neural networks are as effective as certified dermatologists in differentiating benign neoplasms from malignant neoplasms on photographic and dermatoscopic images [30]. AI can be used to evaluate electrocardiograms [31] and ultrasound images [32], and in pathomorphology [33] and genomics [34]. AI is a promising tool that can be used to trace contacts during the pandemic, to improve pneumonia diagnostics [35], or to monitor COVID-19 patients [36].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management

Butova

Shayakhmetov

Федин

et al. 2021

JPM

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Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care.

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“…The encouragement of semisupervised learning as well as self-supervised learning could prompt the utilization of AI in arrhythmia more widely, as a self-supervised learning method only requires the operator to input a small number of typical data in order for computers to be able to automatically extract unlabeled data without supervision (26,27). Its application eliminates the need for manual recognition and, in the foreseeable future, could probably become mature enough to be utilized in in-hospital monitoring, radiofrequency ablation operations, and portable devices (28). For patients with angina, this monitoring could not only monitor the onset of myocardial infraction (MI), but could also help in predicting it.…”

mentioning

confidence: 99%

Research output of artificial intelligence in arrhythmia from 2004 to 2021: a bibliometric analysis

Huang¹,

Liu²,

Huang³

et al. 2022

J Thorac Dis

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Background: With the advancement in machine learning (ML) and artificial neural networks as well as the development of portable electrocardiogram devices, artificial intelligence (AI) has been increasing in popularity over the years. In this study, we aimed to provide an overview of the research regarding the utilization of AI techniques to improve the diagnosis of arrhythmia.Methods: We extracted data published 2004 to 2021 from Web of Science database. The online analytic platform, Literature Metrology (http://bibliometric.com), was used to analyze publication trends, including information about journals, authors, institutions, collaborations between countries, citations, and keywords.Results: Keywords, such as deep learning, electrocardiogram (ECG), and convolutional neural network, have been increasing in frequency over the years. The analysis outcomes demonstrated that topics associated with AI, robotic prosthesis, and big data analysis for arrhythmia have become increasingly popular since 2016. Our study also found that atrial fibrillation (AF) and ventricular arrhythmia were the two ECG signal sharing the most interest. Conclusions:The utility of deep learning in diagnostics and the prognostication of arrhythmia has been gaining traction over the years, covering areas from electrocardiogram detection to atrial arrhythmogenesis model construction. Our study revealed the trend of topics from 2004 to 2021, which may help researchers to monitor future trends.

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Artificial Intelligence versus Doctors’ Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography

Cited by 9 publications

References 47 publications

Methodical Evaluation of Healthcare Intelligence for Human Life Disease Detection

Methodical Evaluation of Healthcare Intelligence for Human Life Disease Detection

Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management

Research output of artificial intelligence in arrhythmia from 2004 to 2021: a bibliometric analysis

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