Is machine learning the future for atrial fibrillation screening?

Sivanandarajah, Pavidra; Wu, Huiyi; Bajaj, Nikesh; Khan, Sadia; Ng, Fu Siong

doi:10.1016/j.cvdhj.2022.04.001

Cited by 2 publications

(2 citation statements)

References 55 publications

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“…There may be long-term clinical benefits of some cardiovascular disease screenings, such as in the case of atrial fibrillation or heart failure. Currently it is not cost effective to conduct systematic atrial fibrillation screening; however, AI-automated atrial fibrillation diagnosis has been achieved using a variety of rhythm modalities, including 12-lead ECGs, ambulatory ECGs, and photoplethysmography, and seems to improve cost effectiveness [108].…”

Section: Improvement Of Cost-effectivenessmentioning

confidence: 99%

Current and Future Use of Artificial Intelligence in Electrocardiography

Sellés

Marina-Breysse

2023

JCDD

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Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.

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Section: Improvement Of Cost-effectivenessmentioning

confidence: 99%

Current and Future Use of Artificial Intelligence in Electrocardiography

Sellés

Marina-Breysse

2023

JCDD

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“…Otherwise, if the number of input variables is too high, it is likely that there could be some overfitting [ 54 , 55 , 56 ] in the model, which will likely translate into poor generalization power. Machine learning techniques are well known techniques and have been applied to the study of cardiopathies, see, for instance, work by Sivanandarajah et al [ 57 ] and Kusunose et al [ 58 ].…”

Section: Introductionmentioning

confidence: 99%

Epigenetic Signatures in Hypertension

Perez

Martinez

2023

JPM

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Clear epigenetic signatures were found in hypertensive and pre-hypertensive patients using DNA methylation data and neural networks in a classification algorithm. It is shown how by selecting an appropriate subset of CpGs it is possible to achieve a mean accuracy classification of 86% for distinguishing control and hypertensive (and pre-hypertensive) patients using only 2239 CpGs. Furthermore, it is also possible to obtain a statistically comparable model achieving an 83% mean accuracy using only 22 CpGs. Both of these approaches represent a substantial improvement over using the entire amount of available CpGs, which resulted in the neural network not generating accurate classifications. An optimization approach is followed to select the CpGs to be used as the base for a model distinguishing between hypertensive and pre-hypertensive individuals. It is shown that it is possible to find methylation signatures using machine learning techniques, which can be applied to distinguish between control (healthy) individuals, pre-hypertensive individuals and hypertensive individuals, illustrating an associated epigenetic impact. Identifying epigenetic signatures might lead to more targeted treatments for patients in the future.

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Is machine learning the future for atrial fibrillation screening?

Cited by 2 publications

References 55 publications

Current and Future Use of Artificial Intelligence in Electrocardiography

Current and Future Use of Artificial Intelligence in Electrocardiography

Epigenetic Signatures in Hypertension

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