ArticleScientific reports2021
Accessory pathway analysis using a multimodal deep learning model.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 30 citations in OpenAlex.
- Comparative Interpretation of Pediatric Electrocardiograms by Large Language Models and a Pediatrician.Indian pediatrics · 2026Article
- Benchmarking General Purpose Artificial Intelligence for Accessory Pathway Localisation on 12-Lead Electrocardiograms: A Proof-of-Concept Study.Journal of clinical medicine · 2026Article
- Artificial Intelligence Enabled Noninvasive Mapping of Cardiac Arrhythmia Origins Using the 12-Lead Electrocardiogram.Journal of cardiovascular electrophysiology · 2026Review
- [Wolff-Parkinson-White syndrome : Comparison of different algorithms].Herzschrittmachertherapie & Elektrophysiologie · 2026Review
- Explainable deep learning-based classification of Wolff-Parkinson-White electrocardiographic signals.Frontiers in physiology · 2026Article
- Artificial intelligence in pediatric arrhythmias: current landscape, unique challenges, and translational perspectives.Frontiers in pediatrics · 2026Review
- Utilizing multimodal artificial intelligence to advance cardiovascular diseases.Precision clinical medicine · 2025Review
- Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- A computational study on the influence of antegrade accessory pathway location on the 12-lead electrocardiogram in Wolff-Parkinson-White syndrome.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Review of multimodal machine learning approaches in healthcare.An international journal on information fusion · 2025Article
- Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review.Children (Basel, Switzerland) · 2024Review
- Different patterns of pre-excitation in a large Italian cohort of asymptomatic non-competitive athletes evaluated by telecardiology screening: Prevalence and ECG features.International journal of cardiology. Heart & vasculature · 2024Article
- Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review.JACC. Advances · 2023Article
- EASY-WPW: a novel ECG-algorithm for easy and reliable localization of manifest accessory pathways in children and adults.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2023Article
- Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Accuracy of Algorithms Predicting Accessory Pathway Localization in Pediatric Patients with Wolff-Parkinson-White Syndrome.Children (Basel, Switzerland) · 2022Article
- Artificial Intelligence in Pediatric Cardiology: A Scoping Review.Journal of clinical medicine · 2022Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
Corrections and comments
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Authors and funding
18 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac accessory pathways (APs) in Wolff-Parkinson-White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.