Left Ventricular Ejection Fraction Reporting Variability and Artificial intelligence-Assisted Reproducibility: A Multicenter Analysis.
Authors
Affiliations (13)
Affiliations (13)
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
- Siemens Shenzhen Magnetic Resonance, MR Collaboration NE Asia, Shenzhen, Guangdong 518000, China.
- Department of Radiology, Binzhou People's Hospital, Binzhou, Shandong 256600, China.
- Department of Medical Imaging, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, Fujian 363000, China.
- Department of Radiology, Sanmenxia Central Hospital, Sanmenxia, Henan 472000, China.
- CT/MRI Department, Nanchong Hospital of Beijing Anzhen Hospital Affiliated to Capital Medical University, Nanchong Central Hospital, Nanchong, Sichuan 637000, China.
- Department of Radiology, Huludao Second People's Hospital, Huludao, Liaoning 125000, China.
- College of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai 200000, China.
- Academy of Medical Engineering and Translational Medicine, Tianjin University; R&D Center, Beijing Shuxi Technology Co., Ltd, Beijing 100050, China.
- Technology Research and Development Department, Beijing Shuxi Technology Co., Ltd, Beijing 100050, China.
- Xiantao Song. Department of Cardiology, Beijing Friendship Hospital, Capital Medical University, 95 Yongan Road, Beijing 100050, China. Electronic address: [email protected].
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China. Electronic address: [email protected].
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048 USA.
Abstract
Artificial intelligence (AI) -assisted assessment of left ventricular ejection fraction (LVEF) has been Increasingly adopted in clinical practice. This research aimed to assess the reliability and reproducibility of AI - assisted LVEF assessment in a diverse, real-world, multicenter setting. We conducted a retrospective multicenter study involving 354 CMR examinations. A standardized LVEF reassessment (M-LVEF) was performed using QMass 8.1 and systematically compared with original report-derived LVEF values (R-LVEF) generated by vendor-specific AI tools. For inter-observer reproducibility assessment, three operators independently analyzed 30 randomly selected cases using both fully manual and AI-assisted modes, the latter also performed with QMass 8.1. Operator A performed two measurements in both modes for intra-observer analysis. Both in overall and single-center analysis, the consistency between R-LVEF and M-LVEF was good or excellent (ICC≥0.86), but the 95% limits of agreement all exceeded ± 5% . 44.3% of cases exhibited >5% differences between R-LVEF and M-LVEF, and 17.5% showed >10% differences. The AI-assisted method not only significantly reduced LVEF assessment time compared with manual analysis, but also demonstrated superior reproducibility. This was evidenced by higher inter-observer agreement among three operators (ICC: 0.968, 0.974, 0.984 for AI vs. 0.913, 0.924, 0.971 for manual) and greater intra-observer reproducibility, all with significantly lower coefficients of variation (5.6%, 8.8%, 9.3% vs 2.5%, 3.8%, 4.4%; 3.5% vs 1.0%, P < 0.05). There are individual differences in AI-assisted LVEF assessment, but it has significant advantages in efficiency and reproducibility, making AI a worthwhile tool to facilitate CMR quantification.