{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["44"],"submitter":["Hirota N"],"pubmed_abstract":["<h4>Background</h4>There is increasing evidence that 12-lead electrocardiograms (ECG) can be used to predict biological age, which is associated with cardiovascular events. However, the utility of artificial intelligence (AI)-predicted age using ECGs remains unclear.<h4>Methods</h4>Using a single-center database, we developed an AI-enabled ECG using 17 042 sinus rhythm ECGs (SR-ECG) to predict chronological age (CA) with a convolutional neural network that yields AI-predicted age. Using the 5-fold cross validation method, AI-predicted age deriving from the test dataset was yielded for all ECGs. The incidence by AgeDiff and the areas under the curve by receiver operating characteristic curve with AI-predicted age for cardiovascular events were analyzed.<h4>Results</h4>During the mean follow"],"journal":["International journal of cardiology. Heart & vasculature"],"pagination":["101172"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9841236"],"repository":["biostudies-literature"],"pubmed_title":["Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms."],"pmcid":["PMC9841236"],"pubmed_authors":["Suzuki S","Arita T","Hirota N","Motogi J","Satoh K","Hyodo A","Takayanagi T","Yamashita T","Nakai H","Matsuzawa W","Yagi N","Otsuka T","Umemoto T"],"additional_accession":[]},"is_claimable":false,"name":"Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms.","description":"<h4>Background</h4>There is increasing evidence that 12-lead electrocardiograms (ECG) can be used to predict biological age, which is associated with cardiovascular events. However, the utility of artificial intelligence (AI)-predicted age using ECGs remains unclear.<h4>Methods</h4>Using a single-center database, we developed an AI-enabled ECG using 17 042 sinus rhythm ECGs (SR-ECG) to predict chronological age (CA) with a convolutional neural network that yields AI-predicted age. Using the 5-fold cross validation method, AI-predicted age deriving from the test dataset was yielded for all ECGs. The incidence by AgeDiff and the areas under the curve by receiver operating characteristic curve with AI-predicted age for cardiovascular events were analyzed.<h4>Results</h4>During the mean follow","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2025-04-04T14:28:22.029Z","creation":"2025-04-04T14:28:22.029Z"},"accession":"S-EPMC9841236","cross_references":{"pubmed":["36654885"],"doi":["10.1016/j.ijcha.2023.101172"]}}