Anatomy education is at the forefront of integrating innovative technologies into its curricula. However, despite this rise in technology numerous authors have commented on the shortfall in efficacy studies to assess the impact such technology-enhanced learning (TEL) resources have on learning. To assess the range of evaluation approaches to TEL across anatomy education, a systematic review was conducted using MEDLINE, the Educational Resources Information Centre (ERIC), Scopus, and Google Scholar, with a total of 3,345 articles retrieved. Following the PRISMA method for reporting items, 153 articles were identified and reviewed against a published framework-the technology-enhanced learning evaluation model (TELEM). The model allowed published reports to be categorized according to evaluations at the level of (1) learner satisfaction, (2) learning gain, (3) learner impact, and (4) institutional impact. The results of this systematic review reveal that most evaluation studies into TEL within anatomy curricula were based on learner satisfaction, followed by module or course learning outcomes. Randomized controlled studies assessing learning gain with a specific TEL resource were in a minority, with no studies reporting a comprehensive assessment on the overall impact of introducing a specific TEL resource (e.g., return on investment). This systematic review has provided clear evidence that anatomy education is engaged in evaluating the impact of TEL resources on student education, although it remains at a level that fails to provide comprehensive causative evidence. Anat Sci Educ 11: 303-319. © 2017 American Association of Anatomists.

We present an efficient neural network method for locating anatomical landmarks in 3D medical CT scans, using atlas location autocontext in order to learn long-range spatial context. Location predictions are made by regression to Gaussian heatmaps, one heatmap per landmark. This system allows patchwise application of a shallow network, thus enabling multiple volumetric heatmaps to be predicted concurrently without prohibitive GPU memory requirements. Further, the system allows inter-landmark spatial relationships to be exploited using a simple overdetermined affine mapping that is robust to detection failures and occlusion or partial views. Evaluation is performed for 22 landmarks defined on a range of structures in head CT scans. Models are trained and validated on 201 scans. Over the final test set of 20 scans which was independently annotated by 2 human annotators, the neural network reaches an accuracy which matches the annotator variability, with similar human and machine patterns of variability across landmark classes.

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A relatively new, fully accredited MSc in Medical Visualisation and Human Anatomy, is now offered through a joint collaboration with the Laboratory of Human Anatomy, University of Glasgow and the Digital Design Studio, Glasgow School of Art. This degree combines training in digital technologies and intensive human anatomy training as a result of a long-standing successful partnership between these two esteemed institutes. The student also has to complete a research dissertation which encompasses both the digital perspective and a related medical, dental, surgical, veterinary (comparative anatomy) or life science specialty to enhance development in the digital field for a variety of specialties. This article discusses the background in development of this degree, the course structure and the career prospects and destinations for graduates of this unique degree programme.

We are revisiting the workflow established during the Head and Neck Anatomy project, for development of an accurate digital model of the full human body anatomy. Our aim is to improve the interface in the head and neck application, thus providing a new set of functionalities (e.g., corner-cut cross section, MRI/CT data viewer & multimedia viewer) to reinforce the interaction paradigm and the exploration of the whole human body anatomy.The acquisition of the anatomical dataset is fully based on MRI/CT data scans of a male cadaveric specimen. Anatomical structures are being classified and isolated from data scans using medical software, such as AMIRA.Additionally, several prosections were carried out to isolate relevant anatomical structures and capture their surface through photogrammetry, a method employed to construct reality look-alike 3D models from photographs. This will allow us to construct an interactive multimedia library of prosected structures to be proposed through the enhanced application interface.

The use of technology‐enhanced learning (TEL) resources is now ubiquitous within anatomy education. Anatomy educators and commercial partners are constantly pushing the boundaries of technological innovation in education, and students now have access to a wide variety of TEL resources from both their own institution and other online sources. This study aimed to identify factors that may influence a student’s decision to utilise TEL resources when studying anatomy. This study, approved by the university ethics committee, was undertaken with Year 2 medical students at a UK university. The study employed surveys and focus groups in an exploratory sequential mixed methods approach. Firstly, pilot Likert scale items were developed following analysis of existing surveys from both anatomy TEL evaluation literature (identified from a previously published systematic literature review) and broader education literature. The resultant pilot survey responses (α=0.891; n=131) were analysed using principal components analysis (PCA). Secondly, the results of the pilot survey were explored in greater depth using focus groups with survey respondents (n=12). Focus groups were audio recorded, transcribed verbatim and analysed using thematic analysis. Finally, triangulation of the data collected from both the pilot survey and focus groups informed the development of a refined survey scale. The consecutive Year 2 medical cohort completed the survey (n=129). PCA was utilised to validate the adapted survey scale (α=0.893), the results of which revealed four emergent factors. Further statistical tests revealed no significant difference in responses when comparing gender, resource preference (i.e. TEL or non‐TEL) and academic achievement. Using a robust mixed method approach, this study developed the validated 23‐item Anatomy TEL Utility Scale with the following emergent factors: (1) Attitude Towards TEL; (2) Perceived Effectiveness; (3) Visual Appeal; (4) Personal and Social Influence. The results from this study revealed that, in addition to affective attitude towards TEL, students’ use of TEL resources for studying anatomy may be influenced by their perceptions of resource usefulness, ease of use and visual appeal. Furthermore, students revealed they are most likely to seek resources that are aligned to their curriculum by using resources provided by their institution, or resources validated by their instructor or peers. These findings highlight the responsibility held by anatomy instructors in ensuring they recommend appropriate TEL resources to students and, if developing their own resources, emphasizes the importance of evidence‐informed instructional design.

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