Current advancement in diagnosing atrial fibrillation by utilizing wearable devices and artificial intelligence: A review study

YC Wang, X Xu, A Hajra, S Apple, A Kharawala… - Diagnostics, 2022 - mdpi.com
Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than
80 years old. The importance of early diagnosis of atrial fibrillation has been broadly …

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

M Salvi, MR Acharya, S Seoni, O Faust… - … : Data Mining and …, 2024 - Wiley Online Library
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 …

Machine learning in cardio-oncology: new insights from an emerging discipline

Y Zheng, Z Chen, S Huang, N Zhang, Y Wang… - Reviews in …, 2023 - vbn.aau.dk
A growing body of evidence on a wide spectrum of adverse cardiac events following
oncologic therapies has led to the emergence of cardio-oncology as an increasingly …

[HTML][HTML] State of the art paper: Cardiac computed tomography of the left atrium in atrial fibrillation

N Bodagh, MC Williams, K Vickneson… - Journal of …, 2023 - Elsevier
The clinical spectrum of atrial fibrillation means that a patient-individualized approach is
required to ensure optimal treatment. Cardiac computed tomography can accurately …

Machine learning in cardiovascular imaging: a scoping review of published literature

P Rouzrokh, B Khosravi, S Vahdati, M Moassefi… - Current Radiology …, 2023 - Springer
Abstract Purpose of Review In this study, we planned and carried out a scoping review of the
literature to learn how machine learning (ML) has been investigated in cardiovascular …

[HTML][HTML] Application effect of low-dose spiral CT on pulmonary nodules and its diagnostic value for benign and malignant nodules

C Zheng, H Wang, Q Liu, D Han, Y Xin… - American Journal of …, 2023 - ncbi.nlm.nih.gov
Objective: This study was designed to determine the application effect of low-dose computed
tomography (LDCT) on detecting pulmonary nodules (PNs) and its diagnostic value for …

Automatic 3D left atrial strain extraction framework on cardiac computed tomography

L Chen, SH Huang, TH Wang, VS Tseng… - Computer Methods and …, 2024 - Elsevier
Background and objective Strain analysis provides insights into myocardial function and
cardiac condition evaluation. However, the anatomical characteristics of left atrium (LA) …

AI-based, automated chamber volumetry from gated, non-contrast CT

AJ Jacob, O Abdelkarim, S Zook, KH Kragholm… - Journal of …, 2023 - Elsevier
Background Accurate chamber volumetry from gated, non-contrast cardiac CT (NCCT)
scans can be useful for potential screening of heart failure. Objectives To validate a new …

[HTML][HTML] AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CACTM) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and …

M Naghavi, D Yankelevitz, AP Reeves… - Journal of …, 2024 - Elsevier
Background Coronary artery calcium (CAC) scans contain actionable information beyond
CAC scores that is not currently reported. Methods We have applied artificial intelligence …

[HTML][HTML] Fully Automated Assessment of Cardiac Chamber Volumes and Myocardial Mass on Non-Contrast Chest CT with a Deep Learning Model: Validation Against …

R Schmitt, CL Schlett, JI Sperl, S Rapaka… - …, 2024 - pmc.ncbi.nlm.nih.gov
Background: To validate the automated quantification of cardiac chamber volumes and
myocardial mass on non-contrast chest CT using cardiac MR (CMR) as a reference …