Key result
AI-DL segmentation shows excellent agreement with manual LVEF analysis while cutting analysis time by ~77%.
Why the study?
Manual post-processing of CMR for LV function and structure is time-consuming, operator-dependent, and limited in reproducibility, necessitating clinical validation of AI-DL segmentation methods against manual analysis.
Does AI-DL segmentation provide equivalent accuracy and improved efficiency compared to manual post-processing for left ventricular function and structure quantification in patients undergoing CMR?
Observational (n=104)
Single-blind
No
Does AI-DL segmentation provide equivalent accuracy and improved efficiency compared to manual post-processing for left ventricular function and structure quantification in patients undergoing CMR?
Effect estimate: ICC 0.951 (95% CI 0.928-0.966)
p-value: p=<0.001
Deep learning-based artificial intelligence utilizing the standard segmentation method provides highly accurate and significantly faster quantification of left ventricular metrics on CMR compared to manual post-processing.
May support faster CMR analysis without loss of accuracy; extends retrospective validation of standard AI-DL segmentation but leaves open need for prospective trials.
Background: Cardiac magnetic resonance (CMR) is the reference standard for evaluating left ventricular (LV) function and structure. Manual post-processing is time-consuming, operator-dependent, and limited in reproducibility. Deep learning-based artificial intelligence (AI-DL) offers automated image segmentation with expert-level accuracy, but requires clinical validation against manual analysis. This study aimed to directly compare two AI-DL segmentation strategies-simplified (trabeculae and papillary muscles included in blood pool) and standard (included in myocardium)-with manual analysis to determine which approach yields superior clinical agreement. Methods: We retrospectively studied 104 patients who underwent CMR. Using both AI-DL (simplified/standard) methods and traditional manual post-processing software, we quantified LV parameters including ejection fraction (EF), end-diastolic volume and index (EDV/EDVI), end-systolic volume and index (ESV/ESVI), stroke volume and index (SV/SI), cardiac output and index (CO/CI), and end-diastolic LV-mass and index (M/MI). Analysis time, correlation, and agreement with manual results were compared. Results: 0.726-0.944) (all P<0.001) as well as narrower Bland-Altman limits of agreement (LOA) (the differences were smaller and not statistically significant, all P>0.05), whereas the simplified method had wider LOA (the differences were larger and statistically significant, all P<0.001). Conclusions: AI-DL enables rapid and reliable quantification of LV function and structure in CMR. Although both simplified and standard methods show strong agreement with manual analysis, the standard method provides better agreement and narrower limits of difference, making it more clinically acceptable. These findings highlight the potential of AI-DL as a practical tool for routine cardiac functional assessment.
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Lin et al. (2026) conducted an observational in Cardiovascular disease (undergoing cardiac magnetic resonance) (n=104). Artificial intelligence deep learning (AI-DL) automated left ventricular segmentation (standard and simplified methods) vs. Manual post-processing segmentation was evaluated on Agreement (intraclass correlation coefficient) for left ventricular ejection fraction (LVEF) between the AI-DL standard method and manual analysis (ICC 0.951, 95% CI 0.928-0.966, p=<0.001). The AI-DL standard segmentation method demonstrated excellent agreement with manual analysis for left ventricular ejection fraction (ICC 0.951) and reduced median analysis time from 217.0 seconds to 50.0 seconds.
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