The integration of virtual reality (VR) in medical and veterinary education has shown significant benefits, enhancing procedural skills and spatial understanding without the need for live subjects (Aksoy & Kilic, 2019; Moro et al. 2017). Research has demonstrated VR’s effectiveness in ultrasound training, showing improvements in technical skills and confidence (Michalski et al. 2019; Starkov et al. 2019; Hu et al. 2020; Zhang et al. 2021). Similarly, Bickmore et al. (2020) reported that VR-based education tools in veterinary medicine increased student engagement and understanding of complex anatomical structures. Despite these advancements, there is limited research on VR applications in veterinary education, and none for the technically complex skill of ultrasound diagnosis of pregnancy in sheep.
Traditional methods for teaching sheep pregnancy scanning involve direct practice on live animals, posing ethical and practical challenges. To address these, we developed the “EweScan” application for a mixed reality spatial computing device (the Apple Vision Pro) to provide a virtual, interactive training environment inclusive of a 3D animal model demonstrating transducer placement and internal sheep anatomy relevant to pregnancy, and a simulation of real-time ultrasound diagnosis of pregnancy in this species.
In addition to facilitating a reduction in animal usage in teaching, we hypothesise that students using this application will demonstrate superior knowledge and long-term retention of the basics of ultrasound diagnostics, anatomical structures relevant to sheep pregnancy, and interpretation of ultrasound imagery used to determine fetal presence and number. Students (N=60) with no prior knowledge or experience of ultrasound pregnancy diagnosis will be instructed on the aforementioned topics using traditional didactic means, with half the cohort then provided with additional training through use of the EweScan mixed reality application on the Apple Vision Pro. We will subsequently assess both groups on their theoretical and practical understanding of pregnancy in ewes and its diagnosis by ultrasound immediately after training and again six weeks later.
It is hoped that this study will support the integration of mixed reality technology in animal science and veterinary education, and thus facilitate improved learning outcomes and animal welfare.