Children's drug research and development incentives and market pricing optimization based on medical imaging.

Due to differences in physiological characteristics and drug metabolism between children and adults, drug efficacy evaluation and safety monitoring in pediatric drug development present significant challenges. This paper proposes a data-driven incentive mechanism for pediatric drug development based on medical imaging data. This approach optimizes drug market pricing through precise imaging data, promoting accessibility and R&D efficiency for pediatric drugs. This study first collects multi-source computed tomography (CT), magnetic resonance imaging (MRI), and X-ray data, focusing on images of common pediatric diseases. After data preprocessing, a convolutional neural network (CNN) is used for feature extraction to extract key image information. Image difference methods and a U-Net image segmentation network are then used to evaluate drug efficacy and safety, quantify efficacy changes, and analyze side effects. Next, a drug efficacy-safety evaluation model is developed, and game theory is employed to design a R&D incentive mechanism. Monte Carlo simulation is combined with risk assessment to comprehensively consider factors such as cost, R&D investment, and market demand during the pricing optimization phase. A dynamic pricing strategy is implemented to ensure both economic benefits and social accessibility of the drug. Experiments have shown that the drug has a good development effect, with an average tumor volume reduction of 32.7% (95% CI: 28.4%-36.9%). The drug's impact on organ volume is within ± 2 cm³, and the market pricing strategy selects a relatively optimal price point.