AI-Augmented Point of Care Ultrasound in Intensive Care Unit Patients: Can Novices Perform a “Basic Echo” to Estimate Left Ventricular Ejection Fraction in This Acute-Care Setting?

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Abstract
Background: Echocardiography is crucial to understanding cardiac function in the In-tensive Care Unit (ICU), often by measuring the left ventricular ejection fraction (LVEF). Traditionally measures of LVEF are completed as part of comprehensive ex-amination by an expert sonographer or cardiologist, but front-line practitioners in-creasingly perform focused point-of-care estimates of LVEF while managing life-threatening illness. The two main echocardiographic windows used to grossly es-timate LVEF are parasternal and apical windows. Artificial intelligence (AI) algo-rithms have recently been developed to assist non-experts in obtaining and interpret-ing point-of-care ultrasound (POCUS) echo images. We tested the feasibility, accuracy and reliability of novice users estimating LVEF using POCUS-AI echo. Methods: A total of 30 novice users (most never holding an ultrasound probe before) received 2 hours of instruction, then scanned ICU patients (10 patients, 80 scans) using the Exo Iris POCUS probe with AI guidance tool. They were permitted up to 5 minutes at-tempting parasternal long axis (PLAX) and apical 4 chamber (A4C) views. AI-reported LVEF results from these scans were compared to gold-standard LVEF obtained by an expert echo sonographer. To further assess accuracy, this so-nographer also scanned another 65 patients using Exo Iris POCUS-AI vs. conventional protocol. Results: Novices obtained images sufficient to estimate LVEF in 96% of pa-tients in < 5 minutes. Novices obtained PLAX views significantly faster than A4C (1.5 min vs. 2.3 min). Inter-rater reliability of LVEF estimation was very high (ICC 0.88-0.94) whether images were obtained by novices or experts. In n=65 patients, POCUS-AI LVEF was highly specific for a decreased LVEF< 40% (SP=90% for PLAX) but only moderately sensitive (SN=56-70%). Conclusion: Estimating cardiac LVEF from AI-enhanced POCUS is highly feasible even for novices in ICU settings, particu-larly using the PLAX view. POCUS-AI LVEF results were highly consistent whether performed by novice or expert. When AI detected a decreased LVEF, it was highly accurate, although a normal LVEF reported by POCUS-AI was not necessarily reas-suring. This POCUS-AI tool could be clinically useful to rapidly confirm a suspected low LVEF in an ICU patient. Further improvements to sensitivity for low LVEF are needed.

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