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.
Authors
Affiliations (2)
Affiliations (2)
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University Yuedong Hospital, Meizhou, China.
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Abstract
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. 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. AI-DL markedly reduced analysis time compared with manual post-processing following standard method (median 50.0 <i>vs.</i> 217.0 s; P<0.001). The AI-DL simplified method underestimated EF (54.2 <i>vs.</i> 60.0), mass indices (131.45 <i>vs.</i> 156.00, 76.45 <i>vs.</i> 91.25) and overestimated volumetric indices (148.10 <i>vs.</i> 118.00, 88.70 <i>vs.</i> 69.80, 62.85 <i>vs.</i> 46.65, 40.00 <i>vs.</i> 28.70, 69.90 <i>vs.</i> 63.30, 41.70 <i>vs.</i> 37.83, 5.35 <i>vs.</i> 4.80, 3.15 <i>vs.</i> 2.80) relative to manual analysis (all P<0.001). In contrast, the standard method showed no significant differences from manual values (58.3 <i>vs.</i> 60.0, 122.80 <i>vs.</i> 118.00, 72.70 <i>vs.</i> 69.80, 46.30 <i>vs.</i> 46.65, 27.75 <i>vs.</i> 28.70, 64.70 <i>vs.</i> 63.30, 37.85 <i>vs.</i> 37.83, 4.90 <i>vs.</i> 4.80, 2.90 <i>vs.</i> 2.80, 163.05 <i>vs.</i> 156.00, 93.55 <i>vs.</i> 91.25) (all P>0.05). Both methods demonstrated strong correlations with manual results, with the standard method achieving higher intraclass correlation coefficient (0.798-0.978 <i>vs.</i> 0.690-0.928) and correlation coefficients (r=0.772-0.952 <i>vs.</i> 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). 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.