ArticleJournal of imaging informatics in medicine2026
Investigation of Concordance between Artificial Intelligence and Manual Measurements of the Cardiac Parameters in Cardiac Magnetic Resonance Imaging.
Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCommercial artificial intelligence (AI) software for cardiac magnetic resonance (CMR) analysis has shown promising internal validation results, but independent external validation in distinct clinical settings remains limited. This study aimed to externally validate a commercially available AI system for biventricular parameters and per-segmental left ventricular wall thickness (LVWT) measurements in a prospective cohort independent from the training data.
methodsIn this prospective study, 100 healthy Chinese volunteers (67 men; age 20-63 years) from a single center (The Central Hospital of Wuhan) underwent 3.0 T CMR with cine short-axis and long-axis sequences. Biventricular parameters and per-segmental LVWT were measured by commercial AI software (SHUKUN) and a single senior cardiac radiologist with 9 years of experience. Paired t tests, Wilcoxon rank-sum tests, interclass correlation coefficient (ICC), and Spearman correlation were performed. Bland-Altman and Bull's-eye plots were generated.
resultsAI and manual measurements showed good to excellent consistency and correlation for most biventricular parameters in males and the entire cohort (ICCs = 0.551-0.922; r = 0.656-0.910; all p < 0.05), except right ventricular ejection fraction (r = 0.374, ICC = 0.330). Female subjects showed greater variability. For per-segmental LVWT, most segments demonstrated good agreement (ICCs = 0.415-0.776; r = 0.436-0.794; all p < 0.05), with suboptimal performance in anterior and apical segments.
conclusionThis independent external validation demonstrates that commercial AI software has good consistency with manual measurements for most cardiac parameters in healthy adults. However, manual verification remains necessary for segmental LVWT analysis, particularly in female subjects. Validation in diseased populations is warranted before clinical implementation.
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