Comparative evaluation of artificial intelligence-deep learning-based cine short-axis segmentation and manual post-processing for left ventricular function and structure in cardiac magnetic resonance.

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: AI-DL markedly reduced analysis time compared with manual post-processing following standard method (median 50.0 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.