Approximately ten percent of newborns require some help with their breathing at birth. About one percent require extensive assistance at birth which needs to be administered by trained personnel. Neonatal resuscitation is taught through a simulation based training program in North America. Such a training methodology is cost and resource intensive which reduces its availability thereby adversely impacting skill acquisition and retention. We implement and present RETAIN (REsuscitation TrAIning for Neonatal residents) -- a video game to complement the existing neonatal training. Being a video game, RETAIN runs on ubiquitous off-the-shelf hardware and can be easily accessed by trainees almost anywhere at their convenience. Thus we expect RETAIN to help trainees retain and retrain their resuscitation skills. We also report on how RETAIN was developed by an interdisciplinary team of six undergraduate students as a three-month term project for a second year university course.
e-Baby @cite_7 is a serious game in which players perform clinical assessment of oxygenation on preterm infants in a virtual isolette. The infants present a range of respiratory impairments from mild to serious. The players were provided with patient history and had to select appropriate tools for clinical assessment. The assessment was made by responding to a series of questions in a multiple-choice format. The questions drove the interaction and served as an assessment of the trainee's knowledge. The game was evaluated by nursing students who had free access to the simulation and was rated highly for its ease of use and as overall efficacy of learning. The goal of e-Baby was acquisition of procedural knowledge pertaining to the clinical assessment. Our goal is to create a game that trains medical personnel on the application of pre-existing knowledge of clinical intervention (resuscitation) in stressful conditions.
@cite_7
1664203118 2023126559 2046985712 179374799
Objective: to evaluate students opinion regarding e-Baby educational technology. Methodology: exploratory descriptive study in which participated a sample composed of 14 nursing Portuguese students that used e-Baby digital educational technology in an extracurricular course. To achieve the aim of the study, the data collection was realized through an opinion instrument in Likert scale including the possibility of commentaries by students. Is was also collected data of participants’ characterization. Results: students made very satisfactory evaluations regarding the game e-Baby, varying since usability acceptation through suggestions of expansion of the game to other nursing themes. Conclusion: serious game e-Baby can be considered a didactic innovation and motivator tool of learning. Besides, it demonstrates have adequate interface in design and educative function aspects, evocating intense interaction between user and computational tool. Background: The application of digital games for training medical professionals is on the rise. So-called ‘serious’ games form training tools that provide a challenging simulated environment, ideal for future surgical training. Ultimately, serious games are directed at reducing medical error and subsequent healthcare costs. The aim was to review current serious games for training medical professionals and to evaluate the validity testing of such games. Methods: PubMed, Embase, the Cochrane Database of Systematic Reviews, PsychInfo and CINAHL were searched using predefined inclusion criteria for available studies up to April 2012. The primary endpoint was validation according to current criteria. Results: A total of 25 articles were identified, describing a total of 30 serious games. The games were divided into two categories: those developed for specific educational purposes (17) and commercial games also useful for developing skills relevant to medical personnel (13). Pooling of data was not performed owing to the heterogeneity of study designs and serious games. Six serious games were identified that had a process of validation. Of these six, three games were developed for team training in critical care and triage, and three were commercially available games applied to train laparoscopic psychomotor skills. None of the serious games had completed a full validation process for the purpose of use. Conclusion: Blended and interactive learning by means of serious games may be applied to train both technical and non-technical skills relevant to the surgical field. Games developed or used for this purpose need validation before integration into surgical teaching curricula. Advances in neonatal care have improved the survival of infants born prematurely although these infants remain at increased risk of adverse neurodevelopmental outcome. The measurement of white matter structure and features of the cortical surface can help define biomarkers that predict this risk. The measurement of these structures relies upon accurate automated segmentation routines, but these are often confounded by neonatal-specific imaging difficulties including poor contrast, low resolution, partial volume effects and the presence of significant natural and pathological anatomical variability. In this work we develop and evaluate an adaptive preterm multi-modal maximum a posteriori expectation-maximisation segmentation algorithm (AdaPT) incorporating an iterative relaxation strategy that adapts the tissue proportion priors toward the subject data. Also incorporated are intensity non-uniformity correction, a spatial homogeneity term in the form of a Markov random field and furthermore, the proposed method explicitly models the partial volume effect specifically mitigating the neonatal specific grey and white matter contrast inversion. Spatial priors are iteratively relaxed, enabling the segmentation of images with high anatomical disparity from a normal population. Experiments performed on a clinical cohort of 92 infants are validated against manual segmentation of normal and pathological cortical grey matter, cerebellum and ventricular volumes. Dice overlap scores increase significantly when compared to a widely-used maximum likelihood expectation maximisation algorithm for pathological cortical grey matter, cerebellum and ventricular volumes. Adaptive maximum a posteriori expectation maximisation is shown to be a useful tool for accurate and robust neonatal brain segmentation. The rising popularity of video games has seen a recent push towards the application of serious games to medical education and training. With their ability to engage players learners for a specific purpose, serious games provide an opportunity to acquire cognitive and technical surgical skills outside the operating room thereby optimizing operating room exposure with live patients. However, before the application of serious games for surgical education and training becomes more widespread, there are a number of open questions and issues that must be addressed including the relationship between fidelity, multi-modal cue interaction, immersion, and knowledge transfer and retention. In this chapter we begin with a brief overview of alternative medical surgical educational methods, followed by a discussion of serious games and their application to surgical education, fidelity, multi-modal cue interaction and their role within a virtual simulations serious games. The chapter ends with a description of the serious games surgical cognitive education and training framework (SCETF) and concluding remarks.