VidAF: A Motion-Robust Model for Atrial Fibrillation Screening From Facial Videos

Liu, Xuenan; Wang, Dingliang; Wong, Alexander; Ma, Liang; Li, Longwei

doi:10.1109/jbhi.2021.3124967

Cited by 14 publications

(4 citation statements)

References 39 publications

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“…Other authors reported on the remote extraction of PPG‐like systolic peak signals from videos of faces. X. Liu, Yang, et al (2022) introduced VidAF, a motion‐robust model for AF screening, which comprised a CNN for detecting pulse signals from facial videos combined with phase‐driven attention mechanism and denoise coding strategy to reduce motion disturbances. A similar approach was employed by Yan et al (2020).…”

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

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“… Publications by subject area [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , …”

Section: Figureunclassified

Non-Contact Vision-Based Techniques of Vital Sign Monitoring: Systematic Review

Saikevičius,

Raudonis,

Dervinis

et al. 2024

Sensors

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The development of non-contact techniques for monitoring human vital signs has significant potential to improve patient care in diverse settings. By facilitating easier and more convenient monitoring, these techniques can prevent serious health issues and improve patient outcomes, especially for those unable or unwilling to travel to traditional healthcare environments. This systematic review examines recent advancements in non-contact vital sign monitoring techniques, evaluating publicly available datasets and signal preprocessing methods. Additionally, we identified potential future research directions in this rapidly evolving field.

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“…AI was also used for AF detection in other modalities. Two studies have demonstrated that AI may enable AF detection using video [36,37]. By extracting a facial PPG signal, they achieved sensitivities of 94% and PPVs of 90-98% in a controlled setting.…”

Section: Af Detection With Aimentioning

confidence: 99%

Artificial intelligence for the detection, prediction, and management of atrial fibrillation

Isaksen

Baumert

Hermans

et al. 2022

Herzschr Elektrophys

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The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.

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VidAF: A Motion-Robust Model for Atrial Fibrillation Screening From Facial Videos

Cited by 14 publications

References 39 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)

Non-Contact Vision-Based Techniques of Vital Sign Monitoring: Systematic Review

Artificial intelligence for the detection, prediction, and management of atrial fibrillation

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