Is Augmented Reality the New Way for Teaching and Learning Veterinary Cardiac Anatomy?

Application of augmented reality models of canine skull in veterinary anatomical education

Veterinary anatomy plays a crucial role in the curriculum for veterinary medicine and surgery. The integration of modern information technology in veterinary education can greatly benefit from innovative tools such as augmented reality (AR) applications. The aim of this study was to develop an accurate and interactive three-dimensional (3D) digital model of an animal skull using AR technology, aiming to enhance the learning of skull anatomy in veterinary anatomy education. In this study, a canine skull specimen was isolated, and the skull bones were scanned using a structured light scanner to create a 3D digital model of the canine skull, which was found to be indistinguishable from the original specimen by measurement of skull proportions. Furthermore, the interactive AR model of the canine skull, displayed using Unity3D, was subjected to testing and evaluation by 60 first-year veterinary medical students attending the gross anatomy of the animal. The students were divided into two groups: the traditional group and AR group. Both groups completed an objective test and a questionnaire. The evaluation of learning effectiveness in the test revealed no significant difference between the traditional group (which learned using textbooks and a canine skull specimen) and AR group (which learned using AR tools). However, in the questionnaire, students displayed high enthusiasm and interest in using the AR tool. Therefore, the application of AR tools can improve students' motivation for learning and enhance the comprehension of anatomical structures in three dimensions. Furthermore, this study exemplifies the use of AR as an auxiliary tool for teaching and learning in veterinary anatomy education.

Estrus Detection and Dairy Cow Identification with Cascade Deep Learning for Augmented Reality-Ready Livestock Farming

Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming.

Development of a three-dimensional computer model of the equine heart using a polyurethane casting technique and in vivo contrast-enhanced computed tomography

INTRODUCTION/OBJECTIVES

Insight into the three-dimensional (3D) anatomy of the equine heart is essential in veterinary education and to develop minimally invasive intracardiac procedures. The aim was to create a 3D computer model simulating the in vivo anatomy of the adult equine heart.

ANIMALS

Ten horses and five ponies.

MATERIALS AND METHODS

Ten horses, euthanized for non-cardiovascular reasons, were used for in situ cardiac casting with polyurethane foam and subsequent computed tomography (CT) of the excised heart. In five anaesthetized ponies, a contrast-enhanced electrocardiogram-gated CT protocol was optimized to image the entire heart. Dedicated image processing software was used to create 3D models of all CT scans derived from both methods. Resulting models were compared regarding relative proportions, detail and ease of segmentation.

RESULTS

The casting protocol produced high detail, but compliant structures such as the pulmonary trunk were disproportionally expanded by the foam. Optimization of the contrast-enhanced CT protocol, especially adding a delayed phase for visualization of the cardiac veins, resulted in sufficiently detailed CT images to create an anatomically correct 3D model of the pony heart. Rescaling was needed to obtain a horse-sized model.

CONCLUSIONS

Three-dimensional computer models based on contrast-enhanced CT images appeared superior to those based on casted hearts to represent the in vivo situation and are preferred to obtain an anatomically correct heart model useful for education, client communication and research purposes. Scaling was, however, necessary to obtain an approximation of an adult horse heart as cardiac CT imaging is restricted by thoracic size.

Technology-Enhanced Preclinical Medical Education (Anatomy, Histology and Occasionally, Biochemistry): A Practical Guide

The recent explosion of technological innovations in mobile technology, virtual reality (VR), digital dissection, online learning platform, 3D printing, and augmented reality (AR) has provided new avenues for improving preclinical education, particularly in anatomy and histology education. Anatomy and histology are fundamental components of medical education that teach students the essential knowledge of human body structure and organization. However, these subjects are widely considered to be some of the most difficult disciplines for healthcare students. Students often face challenges in areas such as the complexity and overwhelming volume of knowledge, difficulties in visualizing body structures, navigating and identifying tissue specimens, limited exposure to learning materials, and lack of clinical relevance. The COVID-19 pandemic has further exacerbated the situation by reducing face-to-face teaching opportunities and affecting the availability of body donations for medical education.To overcome these challenges, educators have integrated various educational technologies, such as virtual reality, digital 3D anatomy apps, 3D printing, and AI chatbots, into preclinical education. These technologies have effectively improved students' learning experiences and knowledge retention. However, the integration of technologies into preclinical education requires appropriate pedagogical approaches and logistics to align with educational theories and achieve the intended learning outcomes.The chapter provides practical guidance and examples for integrating technologies into anatomy, histology, and biochemistry preclinical education. The author emphasizes that every technology has its own benefits and limitations and is best suited to specific learning scenarios. Therefore, it is recommended that educators and students should utilize multiple modalities for teaching and learning to achieve the best outcomes. The chapter also acknowledges that cadaver-based anatomy education is essential and proposes that educational technologies can serve as a crucial complement for promoting active learning, problem solving, knowledge application, and enhancing conventional cadaver-based education.

Current Current Challenges and Future Research Directions in Augmented Reality for Education

The progression and adoption of innovative learning methodologies signify that a respective part of society is open to new technologies and ideas and thus is advancing. The latest innovation in teaching is the use of Augmented Reality (AR). Applications using this technology have been deployed successfully in STEM (Science, Technology, Engineering, and Mathematics) education for delivering the practical and creative parts of teaching. Since AR technology already has a large volume of published studies about education that reports advantages, limitations, effectiveness, and challenges, classifying these projects will allow for a review of the success in the different educational settings and discover current challenges and future research areas. Due to COVID-19, the landscape of technology-enhanced learning has shifted more toward blended learning, personalized learning spaces and user-centered approach with safety measures. The main findings of this paper include a review of the current literature, investigating the challenges, identifying future research areas, and finally, reporting on the development of two case studies that can highlight the first steps needed to address these research areas. The result of this research ultimately details the research gap required to facilitate real-time touchless hand interaction, kinesthetic learning, and machine learning agents with a remote learning pedagogy.

The Past, Present, and Future: A Discussion of Cadaver Use in Medical and Veterinary Education

Cadaver usage in medical training, although controversial, has persisted over centuries. In veterinary education various methods have been proposed to either improve cadaver preservation, reduce cadaver use, or to replace cadavers entirely, but to date few have gained popularity. This manuscript seeks to: (i) describe the history of cadavers in medical and veterinary education; (ii) compare available cadaveric preservation methods; (iii) reflect on applications of cadaver use in the educational setting; (iv) discuss alternatives to traditional cadaver use; and (v) consider the perceptions of the stakeholders who use them.