ArticleEuropean heart journal. Digital health2025
A comparison of artificial intelligence-enhanced electrocardiography approaches for the prediction of time to mortality using electrocardiogram images.
Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Exploratory association between multimodal AI-derived digital biomarkers and in-hospital mortality in adult patients with pneumonia: A proof-of-concept study.PLOS digital health · 2026Article
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- Artificial intelligence-enabled electrocardiography from scientific research to clinical application.EMBO molecular medicine · 2026Review
- Reliability of Artificial Intelligence-enhanced Electrocardiography.medRxiv : the preprint server for health sciences · 2025Article
- Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.Circulation · 2025Article
- Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges.Current atherosclerosis reports · 2025Review
- Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning.Nature cardiovascular research · 2025Article
- Comparison of artificial intelligence-enhanced electrocardiography approaches for the prediction of time to mortality using electrocardiogram images: reply.European heart journal. Digital health · 2025Article
- The emerging role of AI in transforming cardiovascular care.Future cardiology · 2025Article
- Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.medRxiv : the preprint server for health sciences · 2025Article
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Artificial intelligence in Brazilian Primary Health Care: scoping review.Revista brasileira de enfermagem · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Aims: Most artificial intelligence-enhanced electrocardiogram (AI-ECG) models used to predict adverse events including death require that the ECGs be stored digitally. However, the majority of clinical facilities worldwide store ECGs as images. Methods and results: A total of 1 163 401 ECGs (189 539 patients) from a secondary care data set were available as both natively digital traces and PDF images. A digitization pipeline extracted signals from PDFs. Separate 1D convolutional neural network (CNN) models were trained on natively digital or digitized ECGs, with a discrete-time survival loss function to predict Conclusion: Both the image 2D CNN and digitized 1D CNN enable mortality prediction from ECG images, with comparable performance to natively digital 1D CNN. Models trained on natively digital 1D ECGs can also be applied to digitized 1D ECGs, without any significant loss in performance. This work allows AI-ECG mortality prediction to be applied in diverse global settings lacking digital ECG infrastructure.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.