Introduction
Growth and maturation (GAM) monitoring is essential in youth sport and healthcare, particularly for talent identification, training prescription, and injury risk management. Recent advances in mobile technology have enabled the integration of artificial intelligence (AI), including computer vision (CV) and machine learning (ML), into widely accessible personal devices. However, practical and scalable tools for GAM monitoring remain limited.
Methods
We developed a smartphone application, Maturo, using the Dart programming language. The app integrates automated data processing, AI-based analytical functions, and longitudinal growth tracking. The development process included user and expert advisory consultation, system architecture design, implementation of data security and privacy safeguards, interface prototyping, and translation of scientific models into a functional mobile platform.
Results
Maturo enables users to monitor GAM through a smartphone, a device commonly available to athletes, parents, coaches, and healthcare professionals. The app supports automated maturation-related calculations, longitudinal data visualization, and structured reporting. The integration of AI-driven processing facilitates scalable and user-friendly monitoring without requiring advanced technical expertise.
Discussion
This study represents the first phase of a broader project applying AI technologies to youth GAM monitoring. The development of Maturo demonstrates the feasibility of translating growth and maturation science into an accessible digital solution for sport and health settings. Future validation and real-world implementation studies are warranted to evaluate accuracy, usability, and impact.