Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking—a cardiovascular MR study in health and disease - Summary - MDSpire
Advertisement
Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking—a cardiovascular MR study in health and disease
To evaluate and compare manual and AI-based approaches for quantitative strain metrics in healthy volunteers and patients with various cardiac diseases, highlighting the significance of these comparisons in clinical practice.
Key Findings:
AI-based segmentation showed comparable strain metrics to manual methods, indicating potential for clinical adoption.
AI segmentation reduced time and potential human error in contouring, enhancing workflow efficiency.
Standardization in feature tracking methods is still needed to ensure consistent results across studies.
Interpretation:
AI-derived contours can streamline myocardial strain assessment, potentially improving consistency and efficiency in clinical practice.
Limitations:
Lack of standardization in feature tracking methodology.
Variability in strain values due to different post-processing software and analysis techniques, including AI performance variability.
Conclusion:
AI-based approaches for myocardial strain quantification may enhance clinical routine and research, but further standardization is necessary.
New US cardiovascular statistics document persistent gaps in prevention and treatment across hypertension, diabetes, obesity, and cholesterol management, with marked disparities by age, income, and race.
