Dental age prediction from panoramic radiographs using machine learning techniques.

ABSTRACT: Dental age (DA) estimation is a key diagnostic tool in pediatric dentistry, particularly when birth records are unavailable or unreliable. It guides decisions on growth assessment, orthodontic planning, and timing of interventions such as space maintenance or extractions. Unlike skeletal maturity, dental development is less affected by nutritional and environmental factors, making it a reliable marker of biological age. Conventional methods require expert interpretation and are prone to variability. There is growing interest in automated, objective approaches to streamline this process and enhance clinical utility. A total of 550 panoramic radiographs from children aged 3-14 years were labeled into 11 dental age groups based on the AAPD reference chart by two experienced pediatric dentists

SUBMITTER: Salehizeinabadi M 

PROVIDER: S-EPMC12574863 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature