{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Han X"],"funding":["Beijing Municipal Science and Technology Commission","the Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support","National Key Research and Development Program of China"],"pagination":["28"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8851787"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["22(1)"],"pubmed_abstract":["Background
To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.Methods
We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1-Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value"],"journal":["BMC medical imaging"],"pubmed_title":["Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience."],"pmcid":["PMC8851787"],"funding_grant_id":["ZYLX202101","Z201100005620009","2019YFE0107800"],"pubmed_authors":["Xu L","Guo N","Luo N","Hong M","Yang Z","He Y","Cao J","Han X","Jia X","Wang Z"],"additional_accession":[]},"is_claimable":false,"name":"Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience.","description":"Background
To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.Methods
We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1-Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2025-04-05T22:59:08.148Z","creation":"2025-02-19T03:47:06.898Z"},"accession":"S-EPMC8851787","cross_references":{"pubmed":["35177029"],"doi":["10.1186/s12880-022-00756-y"]}}