Lori Bernard

Background: Echocardiography is crucial to understanding cardiac function in the Intensive Care Unit (ICU), often by measuring the left ventricular ejection fraction (LVEF). Traditionally, measures of LVEF are completed as part of a comprehensive examination by an expert sonographer or cardiologist, but front-line practitioners increasingly perform focused point-of-care estimates of LVEF while managing life-threatening illness. The two main echocardiographic windows used to grossly estimate LVEF are parasternal and apical windows. Artificial intelligence (AI) algorithms have recently been developed to assist non-experts in obtaining and interpreting 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 h 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 min to attempt 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 sonographer also scanned another 65 patients using Exo Iris POCUS-AI vs. conventional protocol. Results: Novices obtained images sufficient to estimate LVEF in 96% of patients in <5 min. 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%). Conclusions: Estimating cardiac LVEF from AI-enhanced POCUS is highly feasible even for novices in ICU settings, particularly 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 reassuring. 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.

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.