Evidence map›Paper›PMID 30442428›Full record

ReviewComputers in biology and medicine2019

Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling.

Chris D Cantwell, Yumnah Mohamied, Konstantinos N Tzortzis, Stef Garasto, Charles Houston, Rasheda A Chowdhury, Fu Siong Ng, Anil A Bharath, Nicholas S Peters

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
5.1field-weighted citation impact, top 4% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 52 citations in OpenAlex.

  1. Review
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  6. Incidence and predictors of thermal oesophageal and vagus nerve injuries in Ablation Index-guided high-power-short-duration ablation of atrial fibrillation: a prospective study.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024
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  19. Creation and application of virtual patient cohorts of heart models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Chris D CantwellElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; Department of Aeronautics, Imperial College London, South Kensington Campus, London, UK. Electronic address: c.cantwell@imperial.ac.uk.
Yumnah MohamiedElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK.
Konstantinos N TzortzisElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK.
Stef GarastoElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK.
Charles HoustonElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK.
Rasheda A ChowdhuryElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK.
Fu Siong NgElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK.
Anil A BharathElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK.
Nicholas S PetersElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK.
Imperial College London · GB

Funding

British Heart Foundation PG/15/59/31621British Heart Foundation PG/16/17/32069British Heart Foundation RE/13/4/30184British Heart Foundation RG/16/3/32175Medical Research Council G0900396
6 · The paper itself

Abstract

We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localised destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.

Indexed as

ElectrocardiographyElectrophysiologic Techniques, CardiacMachine LearningModels, CardiovascularAtrial FibrillationCatheter AblationHeart Conduction SystemHumansCardiac arrhythmiaCardiac electrophysiologyDeep learningElectrogramMachine learningPredictive modelling

Identifiers

PMID30442428
PMCPMC6334203
OpenAlexW2896792008

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.