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The goal of this project is to develop a complete, fully detailed 3D interactive model of the human body and systems in the human body, and allow the user to interacts in 3D with all the elements of that system, to teach students about human anatomy. Some organs, which contain a lot of details about a particular anatomy, need to be accurately and fully described in minute detail, such as the brain, lungs, liver and heart. These organs are need have all the detailed descriptions of the medical information needed to learn how to do surgery on them, and should allow the user to add careful and precise marking to indicate the operative landmarks on the surgery location. Adding so many different items of information is challenging when the area to which the information needs to be attached is very detailed and overlaps with all kinds of other medical information related to the area. Existing methods to tag areas was not allowing us sufficient locations to attach the information to. Our solution combines a variety of tagging methods, which use the marking method by selecting the RGB color area that is drawn in the texture, on the complex 3D object structure. Then, it relies on those RGB color codes to tag IDs and create relational tables that store the related information about the specific areas of the anatomy. With this method of marking, it is possible to use the entire set of color values (R, G, B) to identify a set of anatomic regions, and this also makes it possible to define multiple overlapping regions.

With the rapid growth of the autonomous system, deep learning has become integral parts to enumerate applications especially in the case of healthcare systems. Human body vertebrae are the longest and complex parts of the human body. There are numerous kinds of conditions such as scoliosis, vertebra degeneration, and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone. Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime. In this proposed system, we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the modified Vietnamese x-ray imaging. We applied two different approaches including pre-trained APIs and transfer learning with their pre-trained models due to the unavailability of sufficient x-ray medical imaging. The results show that transfer learning is suitable for the modified Vietnamese x-ray imaging data as compared to the pre-trained API models. Moreover, we also explored and analyzed four transfer learning models and two pre-trained API models with our datasets in terms of accuracy, sensitivity, and specificity.

Information technology and artificial intelligence have been applied in practical training worldwide. Especially in the setting of the COVID pandemic, teaching and learning online while ensuring to provide basic knowledge, attitudes, and clinical skills for future health workers must be warranted. Concerning the characteristics of the radiologic technological training, while commercial simulation software and machinery systems are unaffordable and the current practice facilities do not meet the training needs, self-making simulation software can be a potential solution. Our study aimed to evaluate students’ satisfaction and the training effectiveness of using the “Made in Hue” X-ray simulation software. Key words: Xray simulation, Radiographer training, Simulation-Based Learning

Background: Digital learning media have been proven useful tools to meet the increasing needs of students, especially on human anatomy learning. Aims of the study: The study was to evaluate the initial results of the designing and applying in teaching a 3D virtual reality model of human anatomy at Hue University of Medicine and Pharmacy. Methods: A descriptive cross-sectional study was conducted to survey the evaluation of 2nd-year general medical students, at Hue University of Medicine and Pharmacy, in the last two consecutive academic years, including 428 students learning the anatomy of the respiratory and circulatory system with traditional method and 406 students learning with 3D virtual reality model designed by us. Multivariate logistic regression analysis was used to identify related factors. Results: For the 3D virtual model, 83.5% of students rated it attractive and stimulating; 80.0% enjoy learning with 3D modeling; 77.3% think that it is a useful learning resource and 70.9% of learners feel that they can actively self-study with this virtual model. Besides, when learning with the 3D virtual model, the level of stress from learning accounted for less than 16.3% of the learners and the test scores compared to learning with the traditional model were statistically higher, p < 0.05. Conclusion: Our 3D virtual reality model was initially well received by learners and contributed to the improvement of learning for medical students Key words: anatomy, virtual model, 3D model.

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