Key result
Machine learning LVEF measurements by novices and experts strongly correlate with manual assessments with 0% bias.
Why the study?
Machine learning algorithms enabling automatic real-time echocardiographic LVEF assessment had not been evaluated in critically ill patients.
Does a machine learning algorithm provide accurate and reproducible real-time LVEF measurements by novices and experts compared to manual expert assessment in critically ill patients?
Observational (n=95)
No
Does a machine learning algorithm provide accurate and reproducible real-time LVEF measurements by novices and experts compared to manual expert assessment in critically ill patients?
Machine learning-enabled real-time LVEF assessment provides accurate and highly reproducible measurements in critically ill patients, even when performed by novices.
Answers are grounded in this paper and cite their sources.
Supports ML-assisted real-time LVEF by novices in critical care; leaves open prospective validation before practice change.
Background Machine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients. Methods Real-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken by novices (LVEF Nov ) and by experts (LVEF Exp ) were compared with LVEF reference measurements (LVEF Ref ) taken manually by echo experts. Results LVEF Ref ranged from 26 to 80% (mean 54 ± 12%), and the reproducibility of measurements was 9 ± 6%. Thirty patients (32%) had a LVEF Ref < 50% (left ventricular systolic dysfunction). Real-time LVEF Exp and LVEF Nov measurements ranged from 31 to 68% (mean 54 ± 10%) and from 28 to 70% (mean 54 ± 9%), respectively. The reproducibility of measurements was comparable for LVEF Exp (5 ± 4%) and for LVEF Nov (6 ± 5%) and significantly better than for reference measurements ( p < 0.001). We observed a strong relationship between LVEF Ref and both real-time LVEF Exp ( r = 0.86, p < 0.001) and LVEF Nov ( r = 0.81, p < 0.001). The average difference (bias) between real time and reference measurements was 0 ± 6% for LVEF Exp and 0 ± 7% for LVEF Nov . The sensitivity to detect systolic dysfunction was 70% for real-time LVEF Exp and 73% for LVEF Nov . The specificity to detect systolic dysfunction was 98% both for LVEF Exp and LVEF Nov . Conclusion Machine learning-enabled real-time measurements of LVEF were strongly correlated with manual measurements obtained by experts. The accuracy of real-time LVEF measurements was excellent, and the precision was fair. The reproducibility of LVEF measurements was better with the machine learning system. The specificity to detect left ventricular dysfunction was excellent both for experts and for novices, whereas the sensitivity could be improved. Trial registration : NCT05336448. Retrospectively registered on April 19, 2022.
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Varudo et al. (2022) conducted an observational in Critically ill patients requiring echocardiographic evaluation (n=95). Machine learning algorithm for real-time LVEF assessment vs. Manual LVEF measurements by an expert was evaluated on Correlation between real-time LVEF measured by experts and reference manual LVEF (r = 0.86, p=<0.001). Machine learning-enabled real-time measurements of LVEF by experts and novices were strongly correlated with manual expert measurements with a mean bias of 0%.
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