The introduction of an AI-based tool to emergency department clinicians improved the assessment accuracy of left-ventricular ejection fraction compared to a decline in the control group (0.10 vs -0.12).
RCT (n=16)
Single-blind
1:1
No
Does the use of an AI-based ultrasound application as a didactic tool improve the accuracy of LVEF assessment by emergency department clinicians?
Incorporating an AI-based tool as a didactic aid improves the accuracy of LVEF assessment by emergency department clinicians.
Absolute Event Rate: 0.1% vs -0.12%
p-value: p=0.038
Abstract Objectives Incorporating artificial intelligence (AI) into echocardiography operated by clinicians working in the emergency department to accurately assess left‐ventricular ejection fraction (LVEF) may lead to better diagnostic decisions. This randomized controlled pilot study aimed to evaluate AI use as a didactic tool to improve noncardiologist clinicians’ assessment of LVEF from the apical 4‐chamber (A4ch) view. Methods This prospective randomized controlled pilot study tested the feasibility and acceptability of the incorporation of AI as a didactic tool by comparing the ability of 16 clinicians who work in the emergency department to assess LVEF before and after the introduction of an AI‐based ultrasound application. Following a brief didactic course, participants were randomly equally divided into an intervention and a control group. In each of the first and second sessions, both groups were shown 10 echocardiography A4ch clips and asked to assess LVEF. Following each clip assessment, only the intervention group was shown the results of the AI‐based tool. For the final session, both groups were presented with a new set of 40 clips and asked to evaluate the LVEF. Results In the “normal‐abnormal” category evaluation, as related to own baseline accuracy assessment, the intervention group had an improvement in accuracy on 50 consecutive clip assessments compared with a decline in the control group (0.10 vs. −0.12, respectively, p = 0.038). In the “significantly reduced LVEF” category, the intervention group showed significantly less decline in clip assessment as compared to the control group (−0.03 vs. −0.12, respectively, p = 0.050). Conclusions A study involving AI incorporation as a didactic tool for clinicians working in the emergency department appears feasible and acceptable. The introduction of an AI‐based tool to clinicians working in the emergency department improved the assessment accuracy of LVEF as compared to the control group.
Dadon et al. (Thu,) conducted a rct in Left-ventricular ejection fraction (LVEF) assessment (n=16). AI-based ultrasound application (LVivoEF) vs. No AI tool (standard assessment) was evaluated on Percentage change of assessment accuracy in the normal-abnormal category (combined Sessions 2+3 vs. Session 1) (p=0.038). The introduction of an AI-based tool to emergency department clinicians improved the assessment accuracy of left-ventricular ejection fraction compared to a decline in the control group (0.10 vs -0.12).
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