Background: In recent years, artificial intelligence (AI) methods, including deep convolutional neural networks (CNNs), have gained increasing importance in supporting the automated analysis of echocardiograms. The aim of this study was to evaluate the impact of selected image artifacts—motion blur, acoustic shadowing, and speckle noise—on the performance of automatic classification of standard transthoracic echocardiographic (TTE) views using deep learning models. Methods: The analysis included 217 TTE video clips (2170 frames) covering apical views: two-chamber (A2C), three-chamber (A3C), four-chamber (A4C), and five-chamber (A5C). Two convolutional neural network architectures—ResNet-18 and ResNet-34—were applied, initialized with weights pretrained on the ImageNet dataset (transfer learning). In a limited comparative scope, EfficientNet-B0, a ViT model used as a frozen feature extractor combined with Logistic Regression, and a classical HOG + SVM model, were also included as reference methods. Classification performance was evaluated under conditions of controlled image degradation caused by motion blur, acoustic shadowing, and speckle noise. Results: All analyzed artifacts reduced classification performance, although the magnitude of this effect depended on artifact type. Speckle noise proved to be the most destructive, causing performance collapse across all evaluated methods at high severity. Motion blur and acoustic shadowing produced more differentiated degradation profiles. The ResNet models achieved the highest performance under reference conditions; however, after degradation, the ranking of models was no longer stable. In the comparative analysis, HOG + SVM showed the smallest relative performance loss under motion blur and the highest balanced accuracy under severe acoustic shadowing, whereas severe speckle remained critical for all models. Conclusions: Image quality degradation significantly impairs TTE view classification performance, and evaluation based solely on reference-quality images does not fully reflect model robustness to artifacts. These findings indicate the need to complement standard model evaluation with a structured robustness analysis under degraded imaging conditions and highlight the importance of training and validation settings that better reflect real clinical practice.

Introduction: This study aims to assess medical students’ awareness and opinions regarding body donation for anatomy education and research, including concerns about donation and personal considerations for donating one’s body. Material and methods: The anonymous survey, comprising 20 questions, was completed by 528 medical students from across Poland (366 females and 162 males). Participation in the study was voluntary. The Shapiro–Wilk test was used to determine statistical distribution in the analyzed respondents. The chi-square test was used to compare demographic groups. Statistical significance was set at p < 0.05. Results: 35.4% of respondents consider donating their bodies for scientific and educational purposes. More atheists wanted to donate the organs after death than Catholics (p = 0.03). The year of study and placeof residence do not influence the willingness to donate the body for scientific purposes (p = 0.55 and p = 0.23), but females were more willing to donate the organs after death than males (p = 0.002). The willingness to donate body before medical studies affected the willingness to donate body during medical studies (p < 0.001). The lack of respect from students towards cadavers is the main concern regarding body donation (42.4%). 93.9% respondents think that it is not possible to teach the anatomy without the access to a human body. Conclusions: Participation in dissection room classes increases students’ willingness to donate their own bodies. The primary barrier remains the fear of disrespect toward the cadaver. Since learning about the donor’s life story fosters respect, we recommend transitioning from anonymous approach to cadavers toward personalizing donor information and student participation in memorial ceremonies.