Combining classical educational methods with interactive three-dimensional (3D) visualization technology has great power to support and provide students with a unique opportunity to use them in the study process, training, and/or simulation of different medical procedures in terms of a Human Anatomy course. In 2016, Rīga Stradiņš University (RSU) offered students the 3D Virtual Dissection Table “Anatomage” with possibilities of virtual dissection and digital images at the Department of Morphology. The first 3D models were printed in 2018 and a new printing course was integrated into the Human Anatomy curriculum. This study was focused on the interaction of students with digital images, 3D models, and their combinations. The incorporation and use of digital technologies offered students great tools for their creativity, increased the level of knowledge and skills, and gave them a possibility to study human body structures and to develop relationships between basic and clinical studies.

Background and objectives: Cleft lip palate takes the second place among all anomalies. The complex appearance of cytokines and proliferation markers has still not been clarified despite their possible crucial role in cleft tissue. Therefore, the aim of work was the detection of appearance of pro- and anti-inflammatory cytokines and proliferation marker Ki67, and their inter-correlations in cleft affected lip (CAL). Materials and Methods: The lip material was obtained from 16 children aged before primary dentition during plastic surgery. Control was obtained from 7 non-CAL oral tissue. Tissues were stained for IL-1, IL-4, IL-6, IL-8, IL-10 and Ki67 immunohistochemically. Non-parametric statistic, Mann–Whitney and Spearman’s coefficient were used. Results: All cytokines positive cells were observed more into the epithelium. Statistically significant difference was seen between epithelial IL-1, IL-10, IL-8 and Ki67 positive cells and IL-10-, IL-4-containing connective tissue cells in comparison to the control. Strong positive correlation was detected in CAL epithelium between IL-10 and IL-8, IL-10 and IL-4, IL-10 and IL-1, IL-1 and IL-8, IL-1 and IL-4, IL-4 and IL-8, IL-8 and Ki67, IL-10 and Ki67, but moderate—in connective tissue between IL-1 and IL-10, IL-1 and IL-4. Conclusions: The CAL epithelium is the main source for the interleukins. Rich similar expression of IL-1 and IL-10 suggests the balance between pro-and anti-inflammatory tissue response on basis of dysregulated tissue homeostasis (increase of IL-8). The correlations between the different ILs-1, -4, -8, -10 in CAL epithelium seem to indicate the self-protection compensatory mechanism for intensification of local inflammatory-immune response without involvement of IL-6. The correlations between Ki67 and cytokines indicate the involvement of IL-8 and IL-10 in stimulation of cellular proliferation. IL-4 and IL-10 expression from CAL connective tissue simultaneously to IL-1, IL-4 and IL-10 inter-correlations there suggests the intensification of local immune response regulated probably by main pro-inflammatory cytokine—IL-1.

Educational institutions in several countries state that the education sector should be modernized to ensure a contemporary, individualized, and more open learning process by introducing and developing advance digital solutions and learning tools. Visualization along with 3D printing have already found their implementation in different medical fields in Pauls Stradiņš Clinical University Hospital, and Rīga Stradiņš University, where models are being used for prosthetic manufacturing, surgery planning, simulation of procedures, and student education. The study aimed to develop a detailed methodology for the creation of anatomically correct and optimized models for 3D printing from radiological data using only free and widely available software. In this study, only free and cross-platform software from widely available internet sources has been used—“Meshmixer”, “3D Slicer”, and “Meshlab”. For 3D printing, the Ultimaker 5S 3D printer along with PLA material was used. In its turn, radiological data have been obtained from the “New Mexico Decedent Image Database”. In total, 28 models have been optimized and printed. The developed methodology can be used to create new models from scratch, which can be used will find implementation in different medical and scientific fields—simulation processes, anthropology, 3D printing, bioprinting, and education.

The main aim of this study is to present the usage and importance of 3D dissection tools in the teaching and learning of Anatomy and to describe and explain our experience with Anatomage Table in Human Anatomy studies at Rıga Stradiņš University. In 2017–2018 two 3D dissection tools (scalpels) were used every week in work with Anatomage Table during the practical classes. As methods for collecting data were used discussions between students and teachers. Together 200 students of the Faculty of Medicine and Dentistry were involved in this study. It was possible to create incisions and cuts in order to remove and uncover different layers of organic tissues, to move deep inside step by step and find out which structures it was necessary to look for. Afterwards students showed that they were able to place the organs back and reattach the bones, muscles, blood vessels in the body and put the skin back on. Students enjoyed virtual tools in the practical classes and learned the material better. Virtual tools helped students and tutors to easily understand and memorize different anatomy structures. 3D scalpels were useful for different education activities, but the learning experience may be suitable further for the study of real materials.

The choice of technique for the creation of a 3D digital human bone model from natural specimens has a critical impact on the final result and usability of the obtained model. The cornerstone factor in 3D modeling is the number of faces of polygon mesh, along with topological accuracy, as well as resolution and level of detail of the texture map. Three different techniques (3D scanning, photogrammetry, and micro-computed tomography) have been used to create a digital 3D model of the human zygomatic bone. As implementation and use of 3D models can be divided into three main categories—visualization, simulation, and physical replication to obtain a functioning model (implant or prothesis)—the obtained models have been evaluated by the density and topological accuracy of the polygonal mesh, as well as by visual appearance by inspecting the obtained texture map. The obtained data indicate that for biomedical applications and computer biomechanical simulation the most appropriate technique of 3D model obtainment is micro-computed tomography, in its turn for visualization and educational purposes, the photogrammetry technique is a more preferable choice.

The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum–coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology.