The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students

Noam Aronovitz; Itai Hazan; Roni Jedwab; Itamar Ben Shitrit; Anna Quinn; Oren Wacht; Lior Fuchs

doi:10.1371/journal.pone.0299461

The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students

PLoS One. 2024 Mar 28;19(3):e0299461. doi: 10.1371/journal.pone.0299461. eCollection 2024.

Authors

Noam Aronovitz¹, Itai Hazan^{1

2}, Roni Jedwab^{1

2}, Itamar Ben Shitrit^{1

2

3}, Anna Quinn⁴, Oren Wacht⁵, Lior Fuchs^{2

3

6}

Affiliations

¹ Joyce and Irving Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
² Clinical Research Center, Soroka University Medical Center and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
³ Department of Epidemiology, Biostatistics and Community Health, Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel.
⁴ Medical School for International Health in Beer-Sheva, Beer-Sheva, Israel.
⁵ Department of Emergency Medicine, Ben Gurion University of the Negev in Beer- Sheva, Israel.
⁶ Medical Intensive Care Unit, Soroka University Medical Center, Beer-Sheva, Israel.

Abstract

Purpose: Point-of-care ultrasound (POCUS) is a sensitive, safe, and efficient tool used in many clinical settings and is an essential part of medical education in the United States. Numerous studies present improved diagnostic performances and positive clinical outcomes among POCUS users. However, others stress the degree to which the modality is user-dependent, rendering high-quality POCUS training necessary in medical education. In this study, the authors aimed to investigate the potential of an artificial intelligence (AI) based quality indicator tool as a teaching device for cardiac POCUS performance.

Methods: The authors integrated the quality indicator tool into the pre-clinical cardiac ultrasound course for 4th-year medical students and analyzed their performances. The analysis included 60 students who were assigned to one of two groups as follows: the intervention group using the AI-based quality indicator tool and the control group. Quality indicator users utilized the tool during both the course and the final test. At the end of the course, the authors tested the standard echocardiographic views, and an experienced clinician blindly graded the recorded clips. Results were analyzed and compared between the groups.

Results: The results showed an advantage in quality indictor users' median overall scores (P = 0.002) with a relative risk of 2.3 (95% CI: 1.10, 4.93, P = 0.03) for obtaining correct cardiac views. In addition, quality indicator users also had a statistically significant advantage in the overall image quality in various cardiac views.

Conclusions: The AI-based quality indicator improved cardiac ultrasound performances among medical students who were trained with it compared to the control group, even in cardiac views in which the indicator was inactive. Performance scores, as well as image quality, were better in the AI-based group. Such tools can potentially enhance ultrasound training, warranting the expansion of the application to more views and prompting further studies on long-term learning effects.

Copyright: © 2024 Aronovitz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

MeSH terms

Artificial Intelligence
Echocardiography
Humans
Point-of-Care Systems
Students, Medical*
Ultrasonography / methods

Grants and funding

GE Healthcare© provided the POCUS devices used in this study. Lior Fuchs declares that he works as a consultant for GE Healthcare©. However, it's important to note that GE Healthcare© provided support solely in the form of lending the POCUS systems for the research. They did not play any additional roles in the study design, data collection and analysis, decision to publish, or manuscript preparation. The specific roles of Lior Fuchs are detailed in the 'author contributions' section. It's worth highlighting that this research was conducted independently and not in his capacity as a consultant for GE Healthcare©. Additionally, Lior Fuchs did not receive any financial support or salary from GE Healthcare© for the work he contributed to this research.