ArticleCardiovascular digital health journal2021
Neural networks applied to 12-lead electrocardiograms predict body mass index, visceral adiposity and concurrent cardiometabolic ill-health.
Article in Cardiovascular digital health journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram.Genome medicine · 2025Article
- Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease.NPJ digital medicine · 2024Article
- Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network.Scientific reports · 2024Article
- Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.Circulation. Genomic and precision medicine · 2023Article
- Artificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanismsEuropean heart journal. Digital health · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundObesity is associated with electrophysiological remodeling, which manifests as detectable changes on the surface electrocardiogram (ECG).
objectiveTo develop neural networks (NN) to predict body mass index (BMI) from ECGs and test the hypothesis that discrepancies between NN-predicted BMI and measured BMI are indicative of underlying adiposity and/or concurrent cardiometabolic ill-health.
methodsNN models were developed using 36,856 12-lead resting ECGs from the UK Biobank. Two architectures were developed for continuous and categorical BMI estimation (normal weight [BMI <25 kg/m
resultsECGs were available for 17,807 male and 19,049 female participants (mean ages: 61 ± 7 and 63 ± 8 years; mean BMI 26 ± 5 kg/m
conclusionNN models applied to 12-lead ECGs predict BMI with a reasonable degree of accuracy. Discrepancies between NN-predicted and calculated BMI may be indicative of underlying visceral adiposity and concomitant cardiometabolic perturbation, which could be used to identify individuals at risk of cardiometabolic disease.
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Registered trials
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