Use of artificial intelligence as a didactic tool to improve ejection fraction assessment in the emergency department: A randomized controlled pilot study

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.

Use of Artificial Intelligence to Assess Cardiac Function by Echocardiography: Systematic Review of the State of the Art

“…5,19,20,[22][23][24] The technology is applicable in clinical practice, in emergencies, and in remote areas with scarce resources and few experienced professionals. 5,21,25,27 Integration with clinical data…”

Uso da Inteligência Artificial para Avaliação da Função Cardíaca pela Ecocardiografia: Revisão Sistemática do Estado da Arte

“…5,19,20,[22][23][24] A tecnologia é aplicável na prática clínica, em emergências e em áreas remotas com recursos escassos e poucos profissionais experientes. 5,21,25,27…”

Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction

“…Artifcial intelligence (AI) and computational technologies are increasingly utilized across various imaging modalities, including cardiac imaging, by nonexpert operators. Tey assist in decision making and enhancing diagnostic capabilities [9][10][11][12]. In the clinical practice of echocardiography, AI is mainly used in automated tools implemented in high-end devices but less incorporated in systems used for point-of-care testing [13].…”