ObjectivesTo investigate the occurrence and parents’ cognition of accidental injury among pre-school children in Nanchong city, and provide advice and countermeasures to reduce accidental child injuries.MethodsUsing the multi-stage cluster sampling method, a total of 945 students and parents from 3 classes in each of 4 kindergartens in three districts of Nanchong city were surveyed with questionnaire.ResultsA total of 945 questionnaires were issued and 858 valid questionnaires were returned and the effective response rate was 90.79%. The incidence of incidental injury of pre-school children in Nanchong city was 25.99%, with no difference between boys and girls, and no difference between age groups. The top three injuries were falling (35.64%), smashing/touching/squeezing (22.11%), cutting/stabbing (10.56%). The child smashing/touching/squeezing rate was higher in boys than in girls, the difference was statistically significant (χ2=5.549, P=0.018). The top three places of the occurred injury were outdoor (53.67%), home (43.58%), and playground and kindergarten (13.76%). Between parents who possess injury-related knowledge and those who didn’t, there was no difference in the incidence of accidental injuries among their children. Parents often learned from cell phone (23.88%), TV (21.76%) and computer (18.11%). Ways in which they most hoped to learn from were the school advertised education (22.67%), TV (18.80%) and cell phones (17.75%). Of all types of emergency management skills, the top three figures acquired by the largest population were post-burn emergency treatment (72.03%), emergency treatment for traumatic bleeding (52.56%) and emergency treatment of animal bites (37.53%).ConclusionsThe incidence of accident injuries is high in urban areas of Nanchong city. The safety management of home and kindergarten should be strengthened, including schools' safety education and skills training for children and parents.
ObjectiveTo systematically summarize recent advancements in the application of artificial intelligence (AI) in key components of radiotherapy, explore the integration of technical innovations with clinical practice, and identify current limitations in real-world implementation. MethodsA comprehensive analysis of representative studies from recent years was conducted, focusing on the technical implementation and clinical effectiveness of AI in image reconstruction, automatic delineation of target volumes (TV) and organs at risk (OAR), intelligent treatment planning, and prediction of radiotherapy-related toxicities. Particular attention was given to deep learning models, multimodal data integration, and their roles in enhancing decision-making processes. ResultsAI-based low-dose image enhancement techniques had significantly improved image quality. Automated segmentation methods had increased the efficiency and consistency of contouring. Both knowledge-driven and data-driven planning systems had addressed the limitations of traditional experience-dependent approaches, contributing to higher quality and reproducibility in treatment plans. Additionally, toxicity prediction models that incorporate multimodal data enable more accurate, personalized risk assessment, supporting safer and more effective individualized radiotherapy. ConclusionsRadiotherapy is a fundamental modality in cancer treatment. However, achieving precise tumor ablation while minimizing damage to surrounding healthy tissues remains a significant challenge. AI has demonstrated considerable value across multiple technical stages of radiotherapy, enhancing precision, efficiency, and personalization. Nevertheless, challenges such as limited model generalizability, lack of data standardization, and insufficient clinical validation persist. Future work should emphasize the alignment of algorithmic development with clinical demands to facilitate the standardized, reliable, and practical application of AI in radiotherapy.