Cardiac Electrophysiology Studies Based on Image and Machine Learning

Singh, Yashbir; Deepa, .; Wu, Shi-Yi; Friebe, Michael; Tavares, João Manuel R. S.; Hu, Wei-Chih

doi:10.12691/jbet-6-1-1

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…To make it fit nonlinear functions Can suffer from outliers mal tissue on the CT scans [12,13], detection of the nodules on CT images [14,15]. Some of the radiologists highlight the significance of remaining optimistic about the upcoming opportunities of machine learning the revolution.…”

Section: Fast and Easy To Trainmentioning

confidence: 99%

Integration of Machine Learning into the Field of Cardiac Imaging

Mishra¹,

Hu²

2022

Act Scie Medic

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Machine learning (ML) has changed an essential aspect of human life. This is a subdivision of Artificial intelligence where the machines automatically extract valuable information by the databases' patterns. It has been widely used in medical science, and particularly within the area of computational cardiology. Here, in this chapter, we present a brief picture of a machine-learning algorithm that is used for predictive data-driven models. We also emphasize various domains of machine learning application, such as non-invasive imaging modalities. We bring to a close-by reviewing the drawbacks associated with the current application of Machine learning algorithms within computational cardiology.

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Section: Fast and Easy To Trainmentioning

confidence: 99%

Integration of Machine Learning into the Field of Cardiac Imaging

Mishra¹,

Hu²

2022

Act Scie Medic

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“…The data receives information in inner layer first in case of the canonical configuration, which ultimately transformed via multiple hidden layers in a non-linear fashion before computation of final outputs in the final layer. [17].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Machine Learning Integration in Cardiac Electrophysiology

Singh¹

2020

JARDCS

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Atrial fibrillation is a disorder in which there is a chaotic fire of electrical signals from the upper chambers of the heart. The identification of the location of the myocardium responsible for firing these signals and ablation of the area may potentially cure the problem. The electrophysiologists may have to insert the probes or catheters and do the cardiac mapping to identify and analyze the complex heart signals patterns and to identify the location of AF responsible electrical foci. Nowadays, machine learning has become crucial in every technology field. Automation with software using machine-learning algorithms may aid electrophysiologists to do cardiac mapping without struggle and detecting electrical foci by computers. ML algorithms may identify arrhythmia compared to a board-certified cardiologist and can be developed as a very fast and reliable diagnostic tool.

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“…The non-porous polymeric film used for the manufacturing of other PPE materials such as face shield gloves and boots covering film is made with a different type of molding method [7]. The different polymers and their additives are added into the molding to achieve the proper mechanical properties of the final product [8,9,10,11,12]. 3-D printing is also a good method for production of the PPE but it impacts the manufacturing rate.…”

Section: Personal Protective Equipment (Ppe) Manufacturing Optimization With MLmentioning

confidence: 99%

Battle of COVID-19: Where We Can Take The Help Of Machine Learning To Win?

Mansoor¹,

Singh²

2020

Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020)

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COVID-19 is caused by Coronavirus which has been previously linked with 2012 SARS disease. This is an RNA virus. Highly contagious is nature; spreads by air droplet and contact. It needs both air and contact precautions. Interventions to prevent its spread have been tried which includes social distancing, preventing skin contact with the patient and handwashing with soap. Use of sanitizers with >60% alcohol content has been useful. In scientific methods including pre-detection of signs and symptoms help to prevent the spread by quarantining the sick from the healthy for a period of at least 2 weeks. We can use the technology known to us; here, in this short communication, we have focused to show the basic picture of updated technology which is Artificial Intelligence.

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Cardiac Electrophysiology Studies Based on Image and Machine Learning

Cited by 6 publications

References 20 publications

Integration of Machine Learning into the Field of Cardiac Imaging

Integration of Machine Learning into the Field of Cardiac Imaging

Machine Learning Integration in Cardiac Electrophysiology

Battle of COVID-19: Where We Can Take The Help Of Machine Learning To Win?

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