BoneAge

AI-powered Bone Age assessment

BoneAge automates bone age assessment using the Greulich & Pyle atlas method, the gold standard, helping radiologists detect an advanced or delayed age development, additionally saving valuable time while ensuring precision.

Identify thoracic pathologies within seconds

BoneAge is intended to assist in the diagnosis of patients between 3 to 18 years old undergoing X-ray Bone Age exams to assess the maturity of a child's skeletal system in healthcare settings.

A closer look at BoneAge

Without AI
With AI

Indication

An 11-year-old girl presenting for bone age assessment due to signs of early puberty.

Results

BoneAge analysis indicates an advanced bone age compared to the patient’s chronological age.

For Children

Designed for children aged 3 - 18 years undergoing skeletal age assessment.

Comprehensive Body Coverage

BoneAge is designed for frontal hand acquisition using the Greulich & Pyle method.

Our user-friendly summary table streamlines the process and enhances clinician satisfaction, optimizing workflow and improving patient outcomes.

BoneAge’s MAE is
32% lower
than radiologists’¹
Up to
97%
of AI predictions match the ground truth²
Average time gain of
97%
per exam³

Clinical Studies

View all

BoneAge

Clinical Validation of an Artificial Intelligence Software for Bone Age Assessment based on Greulich & Pyle method in a Portuguese Paediatric Cohort.

Simões, A. M., Meneses, J. P., Oliveira, P. G. & Abrantes, J.

European Journal of Radiology
,
June 21, 2025
This is some text inside of a div block.
Clinical Validation of an Artificial Intelligence Software for Bone Age Assessment based on Greulich & Pyle method in a Portuguese Paediatric Cohort.Clinical Validation of an Artificial Intelligence Software for Bone Age Assessment based on Greulich & Pyle method in a Portuguese Paediatric Cohort.

BoneAge

A critical comparative study of the performance of three AI-assisted programs for bone age determination

Pape J., Rosolowski M., Pfäffle R., Beeskow A. B., Gräfe D.

European Radiology
,
November 5, 2024
This is some text inside of a div block.
A critical comparative study of the performance of three AI-assisted programs for bone age determinationA critical comparative study of the performance of three AI-assisted programs for bone age determination

BoneAge

High performances in bone age estimation using an artificial intelligence solution

Nguyen T, Hermann AL, Ventre J, Ducarouge A, Pourchot A, Marty V et al.

Dⅈ
,
April 22, 2023
This is some text inside of a div block.
High performances in bone age estimation using an artificial intelligence solutionHigh performances in bone age estimation using an artificial intelligence solution

Boost your workflow with the power of advanced integrations

Designed with radiologists, our workflow integrations blend effortlessly into daily routines, enhancing speed, clarity, and confidence at every step.

AI-powered Worklist

Worklist now highlights AI results, findings, and automatically prioritizes urgent patient cases.

Shadow Mode

AI results appear on native images, where radiologists can review, accept, or reject, all within their workflow.

Part of Gleamer Copilot

Gleamer Copilot is the all-in-one AI platform that supports radiologists from image to report. It combines powerful detection tools, smart measurements, and structured reporting to boost accuracy and efficiency, all seamlessly integrated into your workflow.

Already implemented in
+2500
Clinical sites
We analyze
+45M
Patients/year
Worldwide presence in
+45
Countries

¹MAE of BoneAge is smaller than that of readers with 0.49 (6 months) vs. 0.72 years (9 months). (Nguyen et al., Diagnostic and Interventional Imaging, 2023)

²Data is based on internal analysis. BoneAge predicts a class that falls within +/-1 G&P class in 97.1% of cases.

³Data based on internal analysis. Reading times decreased on average from 62.8s to 17.7s per exam.

For the latest regulatory information, refer to: https://www.gleamer.ai/privacy-policy