It’s virtually a normal delivery! Revolutionising obstetric teaching with emerging technologies
Year: 2025
Type: article
Abstract: Simulation-based training is a vital tool in obstetrics and gynaecology education, addressing challenges such as limited clinical exposure during short rotation periods and gender-related sensitivities. This need is further underscored by the fact that non-obstetricians and non-midwives often serve as first responders in out-of-hospital and acute care settings where vaginal birth (VB) is imminent. Traditional mannequin-based simulations depend on in-person tutors with demands on time and physical space. In the absence of realistic clinical scenarios, they serve no better than low-fidelity task trainers. Capitalising on recent advancements in affordable virtual reality (VR) head-mounted display (HMD) technology, we developed and iteratively refined a VR-based childbirth training system between 2020 and 2025. Initial user studies demonstrated strong knowledge retention although participants expressed a preference for in-person mannequin-based simulations due to the lack of tactile feedback in VR. To address this limitation, we introduced a mixed reality (MR)-enhanced childbirth training system in 2024. This innovative approach integrates high-precision alignment of virtual elements with physical mannequins and introduces a tactile component to enhance skill acquisition. A pilot study involving 35 Year 4 undergraduate students yielded positive feedback with participants praising the system’s heightened realism and opportunities for peer-to-peer interaction. While both VR and MR have distinct advantages and limitations, the affordability and versatility of modern HMD make VR/MR-based training modules highly scalable and adaptable to diverse scenarios, surpassing the single-task limitations of traditional mannequins. This promises to improve training efficiency and expand access to VB training for a broader range of healthcare professionals.
Authors: Haojie Cheng, Chang Liu, Eng Tat Khoo, Abhiram Kanneganti, Mahesh Choolani, Arundhati Tushar Gosavi
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410906754