AI-Assisted Detection of Short Stature in Paediatric GI Clinic
About the Project
Children attending paediatric GI clinics frequently present with short stature as part of their primary GI condition. However, a significant subset has a co-existing or primary endocrine cause that goes undetected because no systematic, protocol-driven growth screening exists in current GI workflows. Clinicians rely on individual judgment, and growth parameters are collected but not automatically analysed.
This project builds an AI clinical decision-support tool that analyses each patient's growth data, including height standard deviation scores, growth velocity, genetic height potential, and weight pattern, and then automatically flags cases that warrant referral to Paediatric Endocrinology, regardless of the GI diagnosis.
The Team
Team Roles
UI/UX & Frontend Developer
Design and build the clinician-facing interface: patient data-entry forms, growth chart visualisations, and referral summary views.
AI & Backend Developer
Implement the growth-screening algorithms (SDS calculations, growth velocity, referral trigger logic) and the backend data pipeline.