Objectives: The sacrum is a critical bone in various clinical procedures, including caudal epidural blocks, iliosacral screw placements, fetal growth assessment, and sacral nerve stimulation. This study aims to investigate the morphometry and variational morphology of the anatomical formations in the pelvis and dorsal surface of the sacrum. Methods: Morphometric and morphological characteristics of 30 sacral bones of unknown age and sex were examined. Measurements were made using a digital caliper. Results: The mean height of the sacrum was 106.67±10.16 mm, while their mean width was 103.60±6.78 mm. The morphometric analysis revealed that the mean length of the sacral hiatus was 18.51±7.44 mm, and the distance between the sacral cornua was 11.80±2.46 mm. The sacral hiatus was most commonly observed in an inverted ‘U’ shape, while the least common form was bifid. The sacral canal typically displayed a V-shaped morphology. It was determined that the apex of the sacral hiatus most frequently started at the S4 level (80%) compared to the sacral vertebra, and the base of the sacral hiatus mostly ended at the S5 level (93.4%). Conclusion: Morphometry of the sacrum is essential in guiding clinicians, especially in interventions such as anesthesia and orthopedics. Discrepancies in parameter studies conducted in some countries suggest the significance of ethnic factors. Therefore, it is essential for the number of studies to increase and for physicians to follow the parameters of their society regarding the effectiveness of the treatments.

Background/Aims: Anatomy education is of great importance in evaluating the human body as a whole and understanding the normal functioning of organs and systems. Knowledge of human anatomy plays a critical role in the diagnosis of diseases, surgical interventions, evaluation of the health status of patients, and interpretation of the results of advanced imaging techniques. This study aims to evaluate the satisfaction of learning anatomy with the three-dimensional virtual anatomy atlas we developed and to investigate the effect of virtual three-dimensional applications on learning compared to traditional materials. Methods: The 3D applied atlas we developed was sent to the Faculty of Medicine term 1-2, Dentistry term 1, and Physiotherapy and Rehabilitation term 1 students. Students were asked to use the 3D applied atlas in addition to traditional medical education materials in their anatomy learning process. A 9-question Google Forms survey was prepared to evaluate their satisfaction of the application. Results: 471 participants from the Faculty of Dentistry, Medicine Term 1-2, and Physiotherapy Rehabilitation classes participated in our survey. Of the participants, 140 were Faculty of Dentistry students, 70 were Physical Therapy and Rehabilitation (PTR), 172 were Faculty of Medicine 1st Term and 89 were Faculty of Medicine 2nd Term. The number of students who found two-dimensional atlases useful was 198, while the number of students who found 3D models useful was 231. The number of students who found the cadaver model useful was 161. Conclusions: Based on the survey data we obtained, we can say that the students are satisfied with our 3D atlas application and have a positive attitude towards three-dimensional educational materials.

Background: The spleen is an important organ in the evaluation of systemic diseases due to its hematologic and immunologic functions. Changes in splenic volume and surface area have diagnostic significance in various clinical conditions. This study aimed to evaluate age- and sex-related changes in splenic volume and surface area in an adult population. Material: A total of 304 abdominal CT scans (153 females, 151 males) were retrospectively evaluated. Participants aged 19–89 years were stratified by decades. Changes in splenic surface area and volume were analyzed according to age and sex using measurements obtained from these images with the 3D Slicer software package. Results: Splenic volume showed significant differences across age groups in females, whereas no statistically significant age-related difference was observed in males. Splenic surface area differed significantly according to age groups in both sexes. The highest mean splenic volume and surface area were observed in the 19–29 age group in both sexes. The lowest mean values were detected in the 80–89 age group in females and in the 70–79 age group in males. Conclusions: Splenic volume is higher in males than in females. A statistically significant decrease in splenic volume with advancing age was observed in females, whereas only a non-significant decreasing trend was noted in males. A marked reduction in splenic volume and surface area was observed particularly after the age of 70 years. This finding is associated with age-related immune decline and functional hyposplenism and carries clinical significance in terms of increased susceptibility to infections and diminished immune response in elderly individuals. Furthermore, splenic shrinkage should be considered in the assessment of hematological reserve and in the differentiation of changes related to chronic diseases.

Dear Editors, I am excited to see a current discussion in this journal [1]. In another editorial article, the questions and answers directed to Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI) product, about the authorship process contributed to my idea of conducting a similar study [2]. In addition, addressing the editorial question, the second answer partially satisfied me because it was more innovative. Although the answers of ChatGPT in this article are apparent in the text, their contribution is not included in the footnote, explanation, acknowledgment, or reference; in some studies, it is shown in the footnote [3]; in some, it is included in the acknowledgment section [4]. Such contributions or the nature of the support received should also be evaluated and clearly stated. Which section should be included for AI-enabled content such as ChatGPT? Since accessing the previous accesses is impossible, it may be better to copy them to a visual or open access place instead of giving them a reference in the sources. Although many subjects need to be read further and a detailed examination is required, a brief description of the working mechanism should be made. AI's deep learning is to experiment with a given set of inputs and outputs and make suggestions, and when it encounters a new input, it gives it an appropriate output. As I analyze the book chapters [5] that examine the success of AI programs in the process of inventing, producing art, and doing the work of different professional groups, such as lawyers or doctors in some fields, with appropriate learning algorithms, I think that they may have a promising potential for the future in terms of writing articles. In an environment without prejudice, such as the Turing test, there has been much discussion about the superiority of intelligence only when compared to the machine [5]. In addition, the fact that AI provides a contribution whose authorship cannot be detected by similarity or plagiarism programs, which are different software products, makes this situation difficult to understand. In one of the studies contributing to this editorial correspondence, various AI examples with different functions and more capabilities are given, apart from ChatGPT. In addition, while drawing attention to the trust problem, margin of error, and differences in the level of development between programs, it was emphasized that the suitability of using AI applications for general and simple service operations such as article language editing to reduce financial costs should be treated without prejudice [6]. Another article stated that the support to be received from AI after the article was written would be more reliable, controlled, and risk-free [7]. The article that questioned AI's identity on philosophical and ethical grounds was also remarkable [8]. In a different approach, it was stated that with the increase of pseudoscience authors, scientific and unethical situations may be encountered more frequently and different filtering systems should be used as a precaution. Language translation or text editing contributions were seen as an advantage [9]. In these conditions, where ethical problems are not resolved, it is stated that authorship is not correct and that it should be used to increase the quality of the article by making use of its features that facilitate writing [10]. These articles mention general topics about the potential uses of AI in article writing, possible harms, and cautions are mentioned. The study, which listed suggestions for the practical use of AI in authorship, emphasized the lack of creativity and deep analysis power required for authorship [11]. Another study stated that AI could not be accepted as an author because AI could not take responsibility for its writings, did not comply with research ethics and violated copyright law [12]. As I asked the ChatGPT-3.5 model, another researcher who shared its answer with a similar approach stated that it does not see itself as a researcher and author but that its helpful contributions can be used in the writing and subsequent processes [4]. In another article, which deals with topics such as the areas of use of AI in research, the accuracy of ChatGPT was found to be generally positive [13]. In the article on the opportunities and challenges of AI, which offers comprehensive guidance, the authors expressed their concerns about transparency and explainability of authorship [3]. In a different study, the authorship criteria of The International Committee of Medical Journal Editors (ICMJE) and the Committee on Publication Ethics (COPE) were mentioned and it was explained that AI cannot make a significant contribution with data collection and interpretation, cannot approve the final version of the article, and can only collaborate in writing [14]. Another leading study revealed that AI meets only three of the 14 criteria, namely visualization, drafting, and editing, according to CRediT (Contributor Roles Taxonomy), which is important in terms of authorship criteria. The authors shared the reason why ChatGPT could not meet these criteria and their answers when other criteria were queried with ChatGPT. In parallel with the study, the ChatGPT-3.5 model gave the same answer to my questions and stated that it would not be accepted as an author [15]. General concerns and criticisms focus on the fact that AI cannot take responsibility because of erroneous information and that there are no sanctions in unethical situations. Although there is no such reality now, the debate seems to continue in the coming period, even if AI contributes more than humans and is accepted as an author who is given responsibility and punished. These may depend on where the process will evolve with the contributions of lawyers and software developers and the regulations to be taken according to new developments. Therefore, for now, studies for controlled and comprehensive planning...