ArticleComputing in cardiology2023
Deep Learning System for Left Ventricular Assist Device Candidate Assessment from Electrocardiograms.
Article in Computing in cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Clinical Features and Physiological Signals Fusion Network for Mechanical Circulatory Support Need Prediction in Pediatric Cardiac Intensive Care Unit.IEEE journal of biomedical and health informatics · 2025Article
- Real-time On-device Inference for ECG Classification of Ventricular Dysfunction.Conference record. Asilomar Conference on Signals, Systems & Computers · 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
Abstract
Left Ventricular Assist Devices (LVADs) are increasingly used as long-term implantation therapy for advanced heart failure patients, where candidacy assessment is crucial for successful treatment and recovery. A Deep Learning system based on Electrocardiogram (ECG) diagnoses criteria to stratify candidacy is proposed, implementing multi-model processing, interpretability, and uncertainty estimation. The approach includes beat segmentation for single-lead classification, 12-lead analysis, and semantic segmentation, achieving state-of-the-art results on the classification evaluation of each model, with multilabel average AUC results of 0.9924, 0.9468, and 0.9956, respectively, presenting a novel approach for LVAD candidacy assessment, serving as an aid for decision-making.
Identifiers
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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.