Determination of Skeletal Age From Hand Radiographs Using Deep Learning.

ABSTRACT:

Background

Surgeons treating skeletally immature patients use skeletal age to determine appropriate surgical strategies. Traditional bone age estimation methods utilizing hand radiographs are time-consuming.

Purpose

To develop highly accurate/reliable deep learning (DL) models for determination of accurate skeletal age from hand radiographs.

Study design

Cohort Study.

Methods

The authors utilized 3 publicly available hand radiograph data sets for model development/validation from (1) the Radiological Society of North America (RSNA), (2) the Radiological Hand Pose Estimation (RHPE) data set, and (3) the Digital Hand Atlas (DHA). All 3 data sets report corresponding sex and skeletal age. The RHPE and DHA also contain chronological age. After image preprocessing,

SUBMITTER: Bram JT 

PROVIDER: S-EPMC12381387 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature