Toward Paediatric Digital Twins: STELLA-Segmentation Tool for Enhanced Localisation and Labelling of Diagnostic Areas

The growing interest in artificial intelligence applications in real clinical practice has made the development of personalised medicine possible. Digital Twins support diagnosis and treatment by providing an overall view of patients’ health status. The definition of a patient’s digital model requires the integration of different sources of information that contribute to a holistic view of the subject. In this work, we propose a preliminary step toward the definition of paediatric digital twins, providing a tool for Region of Interest identification on X-ray images. In detail, the proposed tool, STELLA (Segmentation Tool for Enhanced Localisation and Labelling of diagnostic Areas), is adopted to automatically detect the bladder and urethra regions on the images obtained from the cystourethrography exam. STELLA pipeline is based on Segment Anything Model (SAM) for the segmentation task and Resnet-18 for masks classification: SAM is leveraged for automatic masks generation and ResNet18 is trained on labelled masks for Regions of Interest classification. This is framed in a larger context, whose aim is to support posterior urethral valves diagnosis.