{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T18:06:53Z","timestamp":1784657213652,"version":"3.55.0"},"reference-count":30,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"7","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Arrhythmia and Electrophysiology"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Catheter ablation is an essential tool for ventricular arrhythmia management, yet sustained procedural success is hindered by the limited ability to identify nonendocardial arrhythmogenic substrates during the procedure. Although delayed enhancement cardiac magnetic resonance imaging is the reference standard for detecting myocardial fibrosis, barriers including cost, workflow complexity, and artifacts in patients with implantable devices limit its preprocedural use. We hypothesized that intracardiac electrograms provide sufficient information to infer scar beyond the endocardial surface and that this information can be harnessed by machine learning techniques.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>This retrospective study included a total of 131\u2009584 cardiac contact electrogram (EGM) signals collected from 46 patients undergoing ventricular arrhythmia ablation. A stratified patient-wise split was used to create the training\/validation set (N=37) and the testing set (N=9), while ensuring a similar distribution of scar types. We developed a novel image-processing workflow to create scar labels using coregistered cardiac magnetic resonance imaging and electroanatomic mapping surface meshes. We developed a transformer-based self-supervised model, EGM2Scar-AI, alongside basic convolutional neural network models using either EGM waveforms or EGM-derived short-time Fourier transform spectrograms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>The average task-specific area under the receiver operating characteristic curve of both the basic and short-time Fourier transform-based convolutional neural networks was 0.729 (0.725\u20130.732) and 0.729 (0.726\u20130.733), respectively, while EGM2Scar-AI performed significantly better with an average area under the receiver operating characteristic curve of 0.822 (0.819\u20130.825) across all 3 scar types. All models performed better on endocardial and mid-myocardial fibrosis identification, with a modest reduction in performance for epicardial fibrosis. Sensitivity improved significantly with the transformer-based architecture without appreciable changes in specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>Our study demonstrates that routine intracardiac electrograms enable the identification of endocardial, mid-myocardial, and epicardial scar using a transformer-based self-supervised deep learning model. Ultimately, our model has the potential to provide magnetic resonance imaging-like fibrosis maps during EP procedures without the need for external imaging and to increase the data set size for patient-specific models in ventricular arrhythmia research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circep.125.014765","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T09:00:19Z","timestamp":1783414819000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying the Presence and Characteristics of Mid-Myocardial and Epicardial Fibrosis From Intracardiac Electrograms in Patients Undergoing Ventricular Arrhythmia Ablation Using a Transformer-Based Self-Supervised Classifier"],"prefix":"10.1161","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8772-5623","authenticated-orcid":true,"given":"Xichong","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Medicine and Cardiovascular Institute, Stanford University, CA (X.L., S.B., S.S., P.G., R.A.A., H.J.C., A.C.P., N.B., P.J.W., S.M.N., A.J.R.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul","family":"Qayyum","sequence":"additional","affiliation":[{"name":"National Heart and Lung Institute, Imperial College London, United Kingdom (A.Q., S.N.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4825-646X","authenticated-orcid":true,"given":"Sabyasachi","family":"Bandyopadhyay","sequence":"additional","affiliation":[{"name":"Department of Medicine and Cardiovascular Institute, Stanford University, CA (X.L., S.B., S.S., P.G., R.A.A., H.J.C., A.C.P., N.B., P.J.W., S.M.N., 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