Automated prediction of isthmus areas in scar‐related atrial tachycardias using artificial intelligence.
Introduction: Ablation of scar‐related reentrant atrial tachycardia (SRRAT) involves identification and ablation of a critical isthmus. A graph convolutional network (GCN) is a machine learning structure that is well‐suited to analyze the irregularly‐structured data obtained in mapping procedures an...
| Published in: | Journal of Cardiovascular Electrophysiology Vol. 35; no. 7; pp. 1401 - 1412 |
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| Main Authors: | , , , , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Jul2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178395177&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178395177 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10453873 GSB jtl: Journal of Cardiovascular Electrophysiology issn: 10453873 maglogo: Y pubinfo: dt: Jul2024 vid: 35 iid: 7 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 178395177 177143650 178395177 178395177 10.1111/jce.16299 178395177 ppf: 1401 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Automated prediction of isthmus areas in scar‐related atrial tachycardias using artificial intelligence. aug: au: Saluja, Deepak Huang, Ziyi Majumder, Jonah Zeldin, Lawrence Yarmohammadi, Hirad Biviano, Angelo Wan, Elaine Y. Ciaccio, Edward J. Hendon, Christine P. Garan, Hasan affil: Division of Cardiology, Columbia University Vagelos College of Physicians and Surgeons, New York New York,, USA sug: subj: Artificial Intelligence Utilization Tachycardia, Atrial Diagnosis Tachycardia, Atrial Surgery Tachycardia, Atrial Physiopathology Cicatrix Physiopathology Cicatrix Diagnosis Automation Predictive Value of Tests Catheter Ablation Methods Action Potentials Machine Learning Treatment Outcomes Human Male Female Middle Age Aged United States Heart Function Tests Heart Rate Signal Processing, Computer Assisted Funding Source Descriptive Statistics Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Introduction: Ablation of scar‐related reentrant atrial tachycardia (SRRAT) involves identification and ablation of a critical isthmus. A graph convolutional network (GCN) is a machine learning structure that is well‐suited to analyze the irregularly‐structured data obtained in mapping procedures and may be used to identify potential isthmuses. Methods: Electroanatomic maps from 29 SRRATs were collected, and custom electrogram features assessing key tissue and wavefront properties were calculated for each point. Isthmuses were labeled off‐line. Training data was used to determine the optimal GCN parameters and train the final model. Putative isthmus points were predicted in the training and test populations and grouped into proposed isthmus areas based on density and distance thresholds. The primary outcome was the distance between the centroids of the true and closest proposed isthmus areas. Results: A total of 193 821 points were collected. Thirty isthmuses were detected in 29 tachycardias among 25 patients (median age 65.0, 5 women). The median (IQR) distance between true and the closest proposed isthmus area centroids was 8.2 (3.5, 14.4) mm in the training and 7.3 (2.8, 16.1) mm in the test group. The mean overlap in areas, measured by the Dice coefficient, was 11.5 ± 3.2% in the training group and 13.9 ± 4.6% in the test group. Conclusion: A GCN can be trained to identify isthmus areas in SRRATs and may help identify critical ablation targets. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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