Big Data & AI for Pediatric Health
Harnessing advanced analytics, intelligent systems, and large-scale clinical data to uncover insights, support decision-making, and improve outcomes across pediatric care.
Georgia Tech researchers are using big data, artificial intelligence, and advanced sensing technologies to better understand pediatric health challenges and identify opportunities for earlier intervention, smarter clinical decisions, and more personalized care.
Explore our active and recent projects supporting pediatric innovation. Click a project below to view details.
Featured Projects
Modeling the Effects of Orthognathic Surgery on Obstructive Sleep Apnea
An AI-driven research project that uses medical imaging and clinical data to predict obstructive sleep apnea risk and evaluate how jaw surgery can improve airway function in pediatric patients.
FUSION: Surgical Risk Prediction
An AI-powered surgical planning project that analyzes clinical and procedural data to predict neurological risks during pediatric spinal fusion and support safer, more informed decision-making.
Advanced Analytic Modeling for Growth and Curve Progression in Idiopathic Scoliosis
A machine learning–driven project that analyzes growth data and biomarkers to predict pediatric scoliosis progression and better understand how key factors influence patient development and spinal curvature changes.
Shriners HQ Project Phase II: GT + Shriners Accelerating Informatics Research
A collaborative HealthIT initiative that enhances pediatric research infrastructure by improving data quality, validating standardized data models, expanding interoperability, and advancing cloud-based clinical analytics tools.
Shriners Sports Medicine Registry
A centralized data registry initiative that integrates clinical and functional outcomes data to enable multi-site sports medicine research, improve tracking, and support evidence-based care.