Explainable AI for ECG-based prediction of cardiac resynchronization therapy outcomes: learning from machine learning?

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.

Who cites it

3 citing papers in PubMed.

1. AI-enhanced approaches for personalized cardiac treatment: insights from ECG data.NPJ systems biology and applications · 2026
   Article
2. Machine Learning-Based Predictive Model of Aortic Valve Replacement Modality Selection in Severe Aortic Stenosis Patients.Medical sciences (Basel, Switzerland) · 2023
   Article
3. The evolving state of cardiac resynchronization therapy and conduction system pacing: 25 years of research at EP Europace journal.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 · 2023
   Review

Corrections and comments

• Comment on
  Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.2023

Authors and funding

2 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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