Monitoring of Pediatric Growth and Dermal Wellness via Blockchain and U-Net Framework

The combination of artificial intelligence and blockchain technology is revolutionizing the healthcare landscape, particularly in pediatric growth monitoring. This study presents a robust framework that leverages the U-Net algorithm for analyzing skin images, enabling accurate evaluation of growth patterns in children. Utilizing sophisticated image segmentation methods, the system achieves a notable accuracy of 96.5% in detecting growth anomalies related to skin characteristics. To safeguard sensitive pediatric health data, blockchain technology is effectively integrated, ensuring secure transmission and storage of information. This innovative strategy not only enhances diagnostic precision but also complies with strict privacy regulations, leading to a remarkable 99% decrease in data breach risks. The framework supports scalable monitoring, allowing healthcare professionals to initiate early interventions and provide tailored care. Preliminary evaluations demonstrate a 40% decrease in manual assessment time, thus improving the overall efficiency of healthcare operations. This combined model has the potential to transform pediatric healthcare by delivering secure assessments that significantly enhance patient outcomes while tackling vital issues concerning data security and integrity.