ArticleFrontiers in cardiovascular medicine2023
Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02997254 (COMparison of Physiological Algorithms for Real-time Evaluation of Atrial Fibrillation), which is not on this map. Cited by 4 papers.
What it found
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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.
COMparison of Physiological Algorithms for Real-time Evaluation of Atrial Fibrillation
Who cites it
4 citing papers in PubMed.
- Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care.Biomedicines · 2025Review
- Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.Circulation. Arrhythmia and electrophysiology · 2025Article
- Identification of atrial myopathy and atrial fibrillation recurrence after ablation using 3D left atrial phasic strain from retrospective gated computed tomography.European heart journal. Imaging methods and practice · 2025Article
- Novel Domain Knowledge-Encoding Algorithm Enables Label-Efficient Deep Learning for Cardiac CT Segmentation to Guide Atrial Fibrillation Treatment in a Pilot Dataset.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Background: Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation. Methods: We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of Results: In independent test cohorts ( Conclusions: Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for large training sets while retaining accuracy in independent test data. Combining ML with domain knowledge may have broad applications.
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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.