Towards fully automated synthetic ECV quantification: an open-access machine learning-based approach for fast blood draw-free CMR

Le résumé fourni par la source

Extracellular volume (ECV) quantification involves time-consuming multi-step post-processing and a blood draw for hematocrit analysis. This study aimed to develop a fully automated blood draw-free, machine learning-based approach for synthetic ECV assessment for non-invasive assessment of diffuse myocardial fibrosis. We retrospectively evaluated a large clinical cohort of 1092 patients who underwent CMR and ECV measurement at 1.5T or 3T. Participants were divided into training (n = 767) and validation (n = 325) cohorts. Manual contouring of T1 maps was used to iteratively develop a neural network segmentation model, which was then applied for automated analysis. Fully-automated synthetic ECV was calculated using validated sex- and field strength-specific models. Agreement was assessed using Student’s t-test, Pearson correlation, Bland–Altman analysis, and classification analysis. Fully-automated synthetic ECV showed strong correlation with conventional ECV (r = 0.79, p < 0.001), with no significant differences (26.9% ± 4.9% vs. 27.3% ± 6.4%, p = 0.056). Bland–Altman analysis indicated minimal mean difference of 0.4% with moderate limits of agreement (LoA) spanning − 7.24% to + 8.07%, with good agreement for values of up to 35% (mean difference 0.1%, LoA: − 5.38% to + 5.23%). Fully automated synthetic ECV offers a blood-free proof-of-concept for large-scale post-processing, supporting consistent and efficient assessment of myocardial fibrosis in research settings, pending further validation for clinical use at higher ECV ranges.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.