{"doi":"10.1101/2025.08.19.25333786","title":"Estimating ascending aortic diameter from the electrocardiogram","abstract":"In an analysis of 69,173 UK Biobank participants, we paired MRI-based measurements of the ascending aortic diameter with ECG signal. We trained a 1D convolutional neural network (ECGAI-TAA) to consume the 10-second 500Hz 12-lead signal and to emit an estimate of the ascending aortic diameter. We assessed model performance in an internal test set of 5,191 participants. The resulting model explained 31% of the variance in aortic diameter, and it couldn't be fully explained by clinical factors such as age, sex, height, weight, pulse rate, blood pressure, or left ventricular mass. Evaluating a clinically relevant diameter threshold (4.0cm, representing dilation), 2.5% of the population had a dilated ascending aorta; when comparing that same proportion of the population (individuals in the top 2.5% of the deep learning model score) to the remaining participants, we found a nearly 16-fold odds ratio for aortic dilation. Using a variational autoencoder-based visualization, we hypothesized that a lateral-superior axis shift may underlie the electrical changes being detected by the model. An important limitation is that these findings represent a physiological observation, not an externally validated risk score. In conclusion, the ECGAI-TAA deep learning model demonstrates that ascending aortic diameter can be, in part, estimated from the 12-lead ECG.","journal":"medRxiv","year":2025,"id":573036,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.914,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":901,"name":"Jeffrey E. Olgin","orcid":"0000-0003-1684-9327","position":1,"is_corresponding":false},{"id":902,"name":"James P. Pirruccello","orcid":"0000-0001-6088-4037","position":2,"is_corresponding":false},{"id":996004,"name":"Zachariah S. Demarais","orcid":null,"position":0,"is_corresponding":true}],"reference_count":3,"raw_metadata":null,"created_at":"2026-07-19T02:57:27.876396Z","pmid":"40894145","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}