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Comparison of Augmented Reality‐Based and Conventional Training Methods for Radiographic Positioning in Second‐Year Radiologic Technology Students in Japan

Year: 2025

Type: article

Abstract: INTRODUCTION: In Japanese training schools for radiologic technologists, improving students' practical skills presents an educational challenge. To address this issue, an augmented reality (AR)-based visual assistance tool for radiographic positioning training was developed. This study aimed to evaluate and compare the effectiveness of a previously developed AR tool with conventional training methods. METHODS: Fifty-seven second-year radiologic technology students were randomly divided into two groups: an experimental group (n = 29) trained with AR and a control group (n = 28) trained with physical auxiliary tools. Both groups practised positioning the torso at 35° and 45° for 1.5 min each. The timeframe aligned with the typical time allocated to each student during traditional hands-on training. Proficiency tests were conducted 1 and 4 weeks after training to evaluate the accuracy of the angles. RESULTS: In the test conducted 1 week after training, the experimental group demonstrated significantly smaller errors than the control group (p = 0.002). However, in the test administered 4 weeks after training, although the experimental group continued to show smaller errors, the difference was not statistically significant (p = 0.066). CONCLUSION: AR-based training can enhance radiographic positioning training. However, a single brief session is considered insufficient for maintaining long-term learning effects. Continuous use of AR is warranted for achieving long-term educational effectiveness. Future research is needed to optimise AR training methods and determine the impact on students' clinical training performance and postgraduate proficiency.

Source: Journal of Medical Radiation Sciences

DOI: 10.1002/jmrs.70019

Author: Shin Nagamata

Institutions: Kobe Tokiwa University, Teikyo University

Language: English

Topic: Augmented Reality Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Quality education, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,930