{"doi":"10.1016/j.jacadv.2024.101249","title":"AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT","abstract":"Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results: < 0.001), with a small bias (-9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR. Conclusions: The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR.","journal":"JACC Advances","year":2024,"id":441123,"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":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9622,"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":1057735,"name":"Aakash Shanbhag","orcid":null,"position":1,"is_corresponding":false},{"id":316500,"name":"Robert J.H. Miller","orcid":"0000-0003-4676-2433","position":2,"is_corresponding":false},{"id":1253568,"name":"Nicholas Kwok","orcid":"0009-0006-6724-1513","position":3,"is_corresponding":false},{"id":1218318,"name":"Parker Waechter","orcid":"0009-0007-8829-1402","position":4,"is_corresponding":false},{"id":1210301,"name":"Valerie Builoff","orcid":null,"position":5,"is_corresponding":false},{"id":259172,"name":"David E. Newby","orcid":"0000-0001-7971-4628","position":6,"is_corresponding":false},{"id":259171,"name":"Damini Dey","orcid":"0000-0003-2236-6970","position":7,"is_corresponding":false},{"id":253731,"name":"Daniel S. Berman","orcid":"0000-0002-3793-9578","position":8,"is_corresponding":false},{"id":259173,"name":"Piotr J. Slomka","orcid":"0000-0002-6110-938X","position":9,"is_corresponding":false},{"id":253724,"name":"Donghee Han","orcid":"0000-0003-1491-3612","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-19T02:01:06.281460Z","pmid":"39309658","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":[]}