{"doi":"10.1101/2024.10.07.24314974","title":"Development and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms","abstract":"Background and Aims: AI-enhanced 12-lead ECG can detect a range of structural heart diseases (SHDs) but has a limited role in community-based screening. We developed and externally validated a noise-resilient single-lead AI-ECG algorithm that can detect SHD and predict the risk of their development using wearable/portable devices. Methods: Using 266,740 ECGs from 99,205 patients with paired echocardiographic data at Yale New Haven Hospital, we developed ADAPT-HEART, a noise-resilient, deep-learning algorithm, to detect SHD using lead I ECG. SHD was defined as a composite of LVEF<40%, moderate or severe left-sided valvular disease, and severe LVH. ADAPT-HEART was validated in four community hospitals in the US, and the population-based cohort of ELSA-Brasil. We assessed the model's performance as a predictive biomarker among those without baseline SHD across hospital-based sites and the UK Biobank. Results: The development population had a median age of 66 [IQR, 54-77] years and included 49,947 (50.3%) women, with 18,896 (19.0%) having any SHD. ADAPT-HEART had an AUROC of 0.879 (95% CI, 0.870-0.888) with good calibration for detecting SHD in the test set, and consistent performance in hospital-based external sites (AUROC: 0.852-0.891) and ELSA-Brasil (AUROC: 0.859). Among those without baseline SHD, high vs. low ADAPT-HEART probability conferred a 2.8- to 5.7-fold increase in the risk of future SHD across data sources (all P<0.05). Conclusions: We propose a novel model that detects and predicts a range of SHDs from noisy single-lead ECGs obtainable on portable/wearable devices, providing a scalable strategy for community-based screening and risk stratification for SHD.","journal":"medRxiv","year":2024,"id":485267,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.958,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":534990,"name":"Lovedeep Singh Dhingra","orcid":"0000-0002-5664-4126","position":1,"is_corresponding":false},{"id":1326923,"name":"Aline F Pedroso","orcid":"0000-0002-1876-8304","position":2,"is_corresponding":false},{"id":1172899,"name":"Sumukh Vasisht Shankar","orcid":"0009-0005-3034-0641","position":3,"is_corresponding":false},{"id":614790,"name":"Andreas Coppi","orcid":"0000-0002-5243-552X","position":4,"is_corresponding":false},{"id":985597,"name":"Akshay Khunte","orcid":"0000-0003-3812-3260","position":5,"is_corresponding":false},{"id":985601,"name":"Murilo Foppa","orcid":"0000-0003-2914-4406","position":6,"is_corresponding":false},{"id":61452,"name":"Luísa Campos Caldeira Brant","orcid":"0000-0002-7317-1367","position":7,"is_corresponding":false},{"id":621705,"name":"Sandhi Maria Barreto","orcid":"0000-0001-7383-7811","position":8,"is_corresponding":false},{"id":986112,"name":"Antonio Luiz P Ribeiro","orcid":null,"position":9,"is_corresponding":false},{"id":1077,"name":"Harlan M. Krumholz","orcid":"0000-0003-2046-127X","position":10,"is_corresponding":false},{"id":89683,"name":"Evangelos K. Oikonomou","orcid":"0000-0003-4362-0720","position":11,"is_corresponding":false},{"id":74880,"name":"Rohan Khera","orcid":"0000-0001-9467-6199","position":12,"is_corresponding":false},{"id":75766,"name":"Arya Aminorroaya","orcid":"0000-0003-3197-2657","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-19T02:07:47.633574Z","pmid":"39417103","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":[]}