Measuring the accuracy of cardiac output using POCUS: the introduction of artificial intelligence into routine care

The Ultrasound Journal

Original Article

Daniela Markovic¹  Oren Friedman²  Sidharth Singh²  Faisal Shaikh³  Peter Yan⁴  Tao He⁵  Nida Qadir⁵  Igor Barjaktarevic⁵  Omar Awan⁶  Jon-Emile Kenny⁷ 

[1] Department of Medicine Statistics, David Geffen School of Medicine at University of California, Los Angeles, CA, USA;Division of Cardiothoracic Surgery, Cedars Sinai Medical Center, Los Angeles, CA, USA;Division of Interventional Pulmonology, Beth Israel Medical Center and Massachusetts General Hospital, Boston, MA, USA;Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine, University of California Los Angeles, 10833 Le Conte Avenue, CHS Building, 43118, 90095, Los Angeles, CA, USA;Division of Pulmonary and Critical Care, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA;Division of Pulmonary and Critical Care, Washington DC VA Medical Center, Washington, DC, USA;Health Sciences North Research Institute, Sudbury, ON, Canada;Flosonics Medical, Sudbury, ON, Canada;

关键词: Velocity time integral;    VTI;    Point-of-care ultrasound;    POCUS;    Hemodynamic monitoring;    Cardiac output;    Artificial intelligence;   

DOI  :  10.1186/s13089-022-00301-6

 received in 2022-04-27, accepted in 2022-12-07,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundShock management requires quick and reliable means to monitor the hemodynamic effects of fluid resuscitation. Point-of-care ultrasound (POCUS) is a relatively quick and non-invasive imaging technique capable of capturing cardiac output (CO) variations in acute settings. However, POCUS is plagued by variable operator skill and interpretation. Artificial intelligence may assist healthcare professionals obtain more objective and precise measurements during ultrasound imaging, thus increasing usability among users with varying experience. In this feasibility study, we compared the performance of novice POCUS users in measuring CO with manual techniques to a novel automation-assisted technique that provides real-time feedback to correct image acquisition for optimal aortic outflow velocity measurement.Methods28 junior critical care trainees with limited experience in POCUS performed manual and automation-assisted CO measurements on a single healthy volunteer. CO measurements were obtained using left ventricular outflow tract (LVOT) velocity time integral (VTI) and LVOT diameter. Measurements obtained by study subjects were compared to those taken by board-certified echocardiographers. Comparative analyses were performed using Spearman’s rank correlation and Bland–Altman matched-pairs analysis.ResultsAdequate image acquisition was 100% feasible. The correlation between manual and automated VTI values was not significant (p = 0.11) and means from both groups underestimated the mean values obtained by board-certified echocardiographers. Automated measurements of VTI in the trainee cohort were found to have more reproducibility, narrower measurement range (6.2 vs. 10.3 cm), and reduced standard deviation (1.98 vs. 2.33 cm) compared to manual measurements. The coefficient of variation across raters was 11.5%, 13.6% and 15.4% for board-certified echocardiographers, automated, and manual VTI tracing, respectively.ConclusionsOur study demonstrates that novel automation-assisted VTI is feasible and can decrease variability while increasing precision in CO measurement. These results support the use of artificial intelligence-augmented image acquisition in routine critical care ultrasound and may have a role for evaluating the response of CO to hemodynamic interventions. Further investigations into artificial intelligence-assisted ultrasound systems in clinical settings are warranted.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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