ArticleEuropean heart journal. Digital health2024
Prospects for artificial intelligence-enhanced electrocardiogram as a unified screening tool for cardiac and non-cardiac conditions: an explorative study in emergency care.
Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Sepsis as a complex syndrome: Are combined biomarkers the future of diagnosis and prognosis? Clinical perspective.Immunology letters · 2026Review
- Factors associated with physician modifications to automated ECG interpretations.European heart journal. Digital health · 2026Article
- Diagnosis of cardiac conditions from 12-lead electrocardiogram through natural language supervision.NPJ digital medicine · 2025Article
- Abnormality prediction and forecasting of laboratory values from electrocardiogram signals using multimodal deep learning.Scientific reports · 2025Article
- Transforming sepsis management: AI-driven innovations in early detection and tailored therapies.Critical care (London, England) · 2025Review
- Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study.Cardio-oncology (London, England) · 2025Article
- Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach.EClinicalMedicine · 2025Article
- A Resting ECG Screening Protocol Improved with Artificial Intelligence for the Early Detection of Cardiovascular Risk in Athletes.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable and externally validated machine learning for neurocognitive diagnosis via ECGs.General psychiatry · 2025Article
- Depression diagnosis from patient interviews using multimodal machine learning.Frontiers in psychiatry · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department. Methods and results: In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner. Conclusion: The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.