Overview

AI estimates skeletal maturity from hand radiographs to support pediatric endocrinology. Automated assessment reduces inter observer variability and speeds reporting. Standardized outputs aid clinical decision making.

Techniques

Deep learning models trained on labeled radiographs predict bone age and provide confidence intervals. Quality control flags poor quality or atypical studies. Integration with growth charts supports interpretation.

Clinical Use

Bone age informs diagnosis of growth disorders and treatment planning. Automated tools streamline workflow in pediatric clinics and hospitals. Validation across populations ensures accuracy.

Ethical Considerations

Models must account for population differences in maturation patterns. Transparent reporting of performance by demographic groups supports equitable care. Clinician oversight remains essential.