{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gallone G"],"funding":["PoC Launchpad"],"pagination":["567-576"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12282367"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["6(4)"],"pubmed_abstract":["Aims
To develop a deep-learning-based system for recognition of subclinical atherosclerosis on a plain frontal chest X-ray.Methods and results
A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest X-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients [58.4% male, median age 63 (51-74) years] with available paired chest X-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on a temporally independent validation cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by th"],"journal":["European heart journal. Digital health"],"pubmed_title":["Detection of subclinical atherosclerosis by image-based deep learning on chest X-ray."],"pmcid":["PMC12282367"],"funding_grant_id":["N 120128—2022.3195"],"pubmed_authors":["Fiandrotti A","Grangetto M","Barbano CA","Fonio P","De Filippo O","Depaoli A","Iannaccone M","Gorrini P","Visciano M","Bruno A","D'Ascenzo F","De Ferrari GM","Presta A","Iodice F","Tore D","Gallone G","Serafini A","Hughes J","Grosso Marra W"],"additional_accession":[]},"is_claimable":false,"name":"Detection of subclinical atherosclerosis by image-based deep learning on chest X-ray.","description":"Aims
To develop a deep-learning-based system for recognition of subclinical atherosclerosis on a plain frontal chest X-ray.Methods and results
A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest X-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients [58.4% male, median age 63 (51-74) years] with available paired chest X-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on a temporally independent validation cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by th","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-03-18T14:02:08.571Z","creation":"2025-08-23T03:06:42.11Z"},"accession":"S-EPMC12282367","cross_references":{"pubmed":["40703133"],"doi":["10.1093/ehjdh/ztaf033"]}}