Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-lead Electrocardiograms

Wang, Xin; Khurshid, Shaan; Choi, Seung Hoan; Friedman, Samuel; Weng, Lu‐Chen; Reeder, Christopher; Singh, Pulkit; Lau, Elizabeth T.; Venn, Rachael A.; Diamant, Nathaniel; Achille, Paolo Di; Philippakis, Anthony; Anderson, Christopher; Ho, Jennifer E.; Ellinor, Patrick T.; Batra, Puneet; Lubitz, Steven A.

doi:10.1101/2022.01.17.22269357

Cited by 2 publications

(2 citation statements)

References 48 publications

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“…In their CNN model, Tang et al (2022) used intracardiac electrograms and 12‐lead ECGs, as well as a multimodal fusion model incorporating electrogram, ECG, and clinical data to predict AF recurrence after ablation. X. Wang et al (2023) analyzed the genetic basis of their ECG‐AI model predictions by performing genome‐wide association studies and correlating the outcomes with the model outputs.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

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Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…hypothesized a genetic basis for an AI‐based ECG in predicting AF risks and performed a genome‐wide association study, finding that the results of the model are correlated with genetic variation implicating sarcomeric, ion channel, and body height pathways. And the inherited predisposition to AF was shown better in predicting AF risk through AI models than clinical models 32 …”

Section: Atrial Fibrillationmentioning

confidence: 99%

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

Guo,

Tian,

Bazoukis

et al. 2024

Pacing Clinical Electrophis

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The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

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Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-lead Electrocardiograms

Cited by 2 publications

References 48 publications

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

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