The Application Effect of 3D Body Technology in the Teaching of Anesthesiology Interns: A Randomized Controlled Study
Journal: Journal of Higher Education Research DOI: 10.32629/jher.v6i2.3807
Abstract
Anesthesiology requires strong anatomical knowledge and operational skills. Traditional 2D teaching methods struggle to display complex 3D structures, hindering theory-to-practice transitions. This study introduced 3D Body technology, using high-precision models and interactive operations to visually demonstrate airways, nerves, and vessels, improving understanding. A randomized trial divided 20 anesthesiology interns into experimental (3D Body) and control (traditional) groups. Results showed the experimental group significantly outperformed in anatomical visualization, operational skills, learning interest, and clinical thinking (P<0.05). Students found 3D Body more intuitive and engaging, while the control group saw limited improvement. 3D Body addresses traditional limitations, enhances clinical training, and supports medical education innovation, with potential for broader application.
Keywords
3D Body technology, anesthesiology teaching, anatomical structure visualization, operational skill training, learning interest stimulation
Full Text
PDF - Viewed/Downloaded: 0 TimesReferences
[1] CHEN C, YANG S, XIONG X, et al. Enhancing Anesthesia Education and Clinical Practice: A Comprehensive Review of GASMAN Simulation Software . Journal of medical education and curricular development, 2024, 11: 1-6.
[2] Wang JZ, Lillia J, Kumar A, Bray P, Kim J, Burns J, Cheng TL. Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review[J]. BMC Bioinformatics. 2022,23(1):431.
[3] YAHIRO D S, CRUZ M P, RIBEIRO B F C, et al. Impact of 3D Printing on Cardiac Surgery in Congenital Heart Diseases: A Systematic Review and Meta-Analysis [J]. Arquivos brasileiros de cardiologia, 2024, 121(12): e20240430.
[2] Wang JZ, Lillia J, Kumar A, Bray P, Kim J, Burns J, Cheng TL. Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review[J]. BMC Bioinformatics. 2022,23(1):431.
[3] YAHIRO D S, CRUZ M P, RIBEIRO B F C, et al. Impact of 3D Printing on Cardiac Surgery in Congenital Heart Diseases: A Systematic Review and Meta-Analysis [J]. Arquivos brasileiros de cardiologia, 2024, 121(12): e20240430.
Copyright © 2025 Junkai Hou, Yifang Shui

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License