ArticlePloS one2018
Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: A proof of concept study.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2023Pooled it
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.European heart journal · 2025Review
- Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy.Journal of clinical medicine · 2025Review
- Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease.Computational and structural biotechnology journal · 2025Article
- Artificial intelligence and atrial fibrillation: A bibliometric analysis from 2013 to 2023.Heliyon · 2024Review
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.Current cardiology reports · 2024Review
- Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review.JACC. Advances · 2023Article
- Machine learning-based risk stratification for mortality in patients with severe aortic regurgitation.European heart journal. Digital health · 2023Article
- Machine learning in the detection and management of atrial fibrillation.Clinical research in cardiology : official journal of the German Cardiac Society · 2022Review
- Artificial intelligence in the diagnosis and management of arrhythmias.European heart journal · 2021Review
- Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.Mayo Clinic proceedings · 2021Article
- Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2021Review
- Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology.Arrhythmia & electrophysiology review · 2020Review
- Deep Learning in Physiological Signal Data: A Survey.Sensors (Basel, Switzerland) · 2020Review
- Pragmatic considerations for fostering reproducible research in artificial intelligence.NPJ digital medicine · 2019Review
- Applications of machine learning in decision analysis for dose management for dofetilide.PloS one · 2019Article
- Deep learning and alternative learning strategies for retrospective real-world clinical data.NPJ digital medicine · 2019Review
- Digital health innovation in cardiology.Cardiovascular digital health journalArticle
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDofetilide is an effective antiarrhythmic medication for rhythm control in atrial fibrillation, but carries a significant risk of pro-arrhythmia and requires meticulous dosing and monitoring. The cornerstone of this monitoring, measurement of the QT/QTc interval, is an imperfect surrogate for plasma concentration, efficacy, and risk of pro-arrhythmic potential.
objectiveThe aim of our study was to test the application of a deep learning approach (using a convolutional neural network) to assess morphological changes on the surface ECG (beyond the QT interval) in relation to dofetilide plasma concentrations.
methodsWe obtained publically available serial ECGs and plasma drug concentrations from 42 healthy subjects who received dofetilide or placebo in a placebo-controlled cross-over randomized controlled clinical trial. Three replicate 10-s ECGs were extracted at predefined time-points with simultaneous measurement of dofetilide plasma concentration We developed a deep learning algorithm to predict dofetilide plasma concentration in 30 subjects and then tested the model in the remaining 12 subjects. We compared the deep leaning approach to a linear model based only on QTc.
resultsFourty two healthy subjects (21 females, 21 males) were studied with a mean age of 26.9 ± 5.5 years. A linear model of the QTc correlated reasonably well with dofetilide drug levels (r = 0.64). The best correlation to dofetilide level was achieved with the deep learning model (r = 0.85).
conclusionThis proof of concept study suggests that artificial intelligence (deep learning/neural network) applied to the surface ECG is superior to analysis of the QT interval alone in predicting plasma dofetilide concentration.
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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.