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Background: The study was to evaluate the effectiveness of dose distribution of four-dimensional computed tomography (4DCT) simulation. Materials and methods: The gross tumor volume (GTV) and clinical target volume (CTV) were contoured in all 10 respiratory phases of 4DCT in 30 patients with non-small cell lung cancer (NSCLC). Both 3D and 4D treatment plans were made individually for each patient using the planning volume (PTV). The PTV3D was taken from a single CTV plus the recommended margin, and the PTV4D was taken from the 4D internal target volume, including all 10 CTVs plus the setup margins. Results: The mean PTV was 460 ± 179 (69-820) cm3 for 3DCT and 401 ± 167 (127-854) cm3 for 4DCT (p = 0.0018). The dose distribution (DD) of organs at risk, especially the lungs, was lower for the 4DCT simulation. The V5%, V10%, and V20% of the total lung dose for 4DCT were significantly lower for the 3DCT. However, lung V30% the heart, esophagus, and spinal cord were not significantly different. In addition, the conformity index and the dose heterogeneity index of the PTV were not significantly different. The normal tissue complication probability (NTCP) of the lung and heart was significantly lower for 4DCT than for 3DCT. Conclusions: The 4DCT simulation gives better results on the NTCP. The organs at risk, especially the lungs, receive a significantly lower DD compared with the 3DCT. The conformity index (CI), heterogeneity index (HI) and the DD to the heart, spinal cord, and esophagus were not significantly different between the two techniques.
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AIM: The aim of this paper is to investigate the incorporation of visual narratives, such as comics and graphics, into nursing education using Generative Artificial Intelligence (GAI) models like DALL-E. BACKGROUND: Visual narratives serve as a powerful method for communicating intricate concepts in nursing education. Despite their advantages, challenges in creating effective educational comics persist due to the need for expertise in graphic design and the associated time and resource constraints. DESIGN: This study examines existing literature that highlights the efficacy of visual narratives in education and demonstrates the potential of GAI models, specifically DALL-E, in creating visual narratives for nursing education. METHODS: We analyze the potential of GAI models, specifically DALL-E, to create visual narratives for educational purposes. This was demonstrated through illustrative examples addressing sensitive topics, illustrating research methodology and designing recruitment posters for clinical trials. Additionally, we discussed the necessity of reviewing and editing the text generated by DALL-E to ensure its accuracy and relevance in educational contexts. The method also considered legal concerns related to copyright and ownership of the generated content, highlighting the evolving legal landscape in this domain. RESULTS: The study found that GAI, specifically DALL-E, has significant potential to bridge the gap in creating visual narratives for nursing education. While offering cost-effectiveness and accessibility, GAI tools require careful consideration of challenges such as text-related errors, misinterpretation of user prompts and legal concerns. CONCLUSIONS: GAI models like DALL-E offer promising solutions for enhancing visual storytelling in nursing education. However, their effective integration requires a collaborative approach, where educators engage with these tools as co-pilots, leveraging their capabilities while mitigating potential drawbacks. By doing so, educators can harness the full potential of GAI to enrich the educational experience for learners through compelling visual narratives.