In this study, a newly-developed model for training veterinary students to inject the jugular vein in horses was evaluated as an additional tool to supplement the current method of teaching. The model was first validated by 19 experienced equine veterinarians, who judged the model to be a realistic and valuable tool for learning the technique. Subsequently, it was assessed using 24 students who were divided randomly into two groups. The injection technique was taught conventionally in a classroom lecture and a live demonstration to both groups, but only group 1 received additional training on the new model. All participants filled out self-assessment questionnaires before and after group 1 received training on the model. Finally, the proficiency of both groups was assessed using an objective structured clinical evaluation (OSCE) on live horses. Students from group 1 showed significantly improved confidence after their additional training on the model and also showed greater confidence when compared to group 2 students. In the OSCE, group 1 had a significantly better score compared to group 2: the median (with inter-quartile range) was 15 (0.7) vs. 11.5 (2.8) points out of 15, respectively. The training model proved to be a useful tool to teach veterinary students how to perform jugular vein injections in horses in a controlled environment, without time limitations or animal welfare concerns. The newly developed training model offers an inexpensive, efficient, animal-sparing way to teach this clinical skill to veterinary students.
The estimation of respiratory rates from contineous respiratory signals is commonly done using either fourier transformation or the zero-crossing method. This paper introduces another method which is based on the autocorrelation function of the respiratory signal. The respiratory signals can be measured either directly using a flow sensor or chest strap or indirectly on the basis of the electrocardiogram (ECG). We compare our method against other established methods on the basis of real-world ECG signals and use a respiration-based breathing frequency as a reference. Our method achieved the best agreement between respiration rates derived from directly and indirectly measured respiratory signals.
In recent years, considerable progress has been made in the non-contact based detection of the respiration rate from video sequences. Common techniques either directly assess the movement of the chest due to breathing or are based on analyzing subtle color changes that occur as a result of hemodynamic properties of the skin tissue by means of remote photoplethysmography (rPPG). However, extracting hemodynamic parameters from rPPG is often difficult especially if the skin is not visible to the camera. In contrast, extracting respiratory signals from chest movements turned out to be a robust method. However, the detectability of chest regions cannot be guaranteed in any application scenario, for instance if the camera setting is optimized to provide close-up images of the head. In such a case an alternative method for respiration detection is required.It is reasonable to assume that the mechanical coupling between chest and head induces minor movements of the head which, like in rPPG, can be detected from subtle color changes as well. Although the strength of these movements is expected to be much smaller in scale, sensing these intensity variations could provide a reasonably suited respiration signal for subsequent respiratory rate analysis.In order to investigate this coupling we conducted an experimental study with 12 subjects and applied motion-and rPPGbased methods to estimate the respiratory frequency from both head regions and chest. Our results show that it is possible to derive signals correlated to chest movement from facial regions. The method is a feasible alternative to rPPG-based respiratory rate estimations when rPPG-signals cannot be derived reliably and chest movement detection cannot be applied as well.
Introduction: A team has been working on a high-fidelity surgical training model for a lumbar disc herniation made of synthetic materials. Since other types of surgical training (as VR Simulators and cadavers) lack optical and haptic realism, the goal was to develop a training model that transfers knowledge about the feeling when operated on and enables training with a realistic workflow. Furthermore, after a training operation, feedback should be provided to the trainee about the applied stress to risk structures during an operation. Methods: The model was designed iteratively with intense validation from surgeons. First, human tissues were developed separately, validated, redesigned and repeated. Then the model was developed in the same process. An intra operating bleeding system and a sensor system were developed and integrated. Results: The surgeons were enthusiastic about the realism of the developed training model. It does not matter which instruments and techniques are preferred, surgeons have realistic haptic response and the surgical workflow follows a real operation when training on the model. This is a huge benefit when compared to VR simulators and cadavers. Discussion: In future steps, stress data to the risk structures will be analyzed, in order to a better understanding of what the critical moments during this operation are and what the stress thresholds of the nerve roots are.
For the development of a new-generation training system for spinal surgery, an iterative development cycle based on four steps was used. By using (i) empathy, (ii) cognitive model, (iii) prototyping and testing, and (iv) validation, an interdisciplinary development team was able to successfully build and validate a training system for lumbar disc surgery. The training system consists of a realistic training model based on synthetic materials and an accompanying training concept. It allows the training of different scenarios, starting from basic surgical tasks to complex surgeries with different complications. This article describes the development process and the results of the validation of the training system.
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