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Updated: Jun 19, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

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Deep learning-based fully automatic Risser stage assessment model using abdominal radiographs.

Jae-Yeon Hwang^1,2,3, Yisak Kim^1,4,5, Jisun Hwang⁶

¹Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.

Pediatric Radiology

|July 24, 2024

Summary

This summary is machine-generated.

A new deep learning model automatically determines Risser stage from abdominal radiographs, achieving high accuracy in classifying skeletal maturity. This AI tool aids in assessing spinal development in adolescents.

Keywords:

Abdominal Child Deep learning Ilium Radiography Risser stage

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Artificial intelligence (AI) shows expert-level performance in medical image classification.
Accurate assessment of skeletal maturity is crucial for orthopedic treatment planning.

Purpose of the Study:

To develop a fully automatic deep learning approach for Risser stage determination using abdominal radiographs.
To evaluate the model's performance in classifying skeletal maturity.

Main Methods:

A multicenter dataset of 1,681 abdominal radiographs (ages 9-18) was retrospectively collected.
A pelvic bone segmentation model (DeepLabv3+ with EfficientNet-B0) extracted iliac crest patches.
A ConvNeXt-B model was trained for Risser stage classification, with performance assessed by accuracy, AUROC, and mean absolute error.

Main Results:

The automatic Risser stage assessment model achieved an accuracy of 0.87 (internal) and 0.75 (external).
Mean absolute error was 0.13 (internal) and 0.26 (external).
Area under the receiver operating characteristic curve (AUROC) reached 0.99 (internal) and 0.95 (external).

Conclusions:

A deep learning-based, fully automatic segmentation and classification model for Risser stage assessment was successfully developed.
The model demonstrates high performance in classifying skeletal maturity from abdominal radiographs.
This AI approach offers a promising tool for objective Risser staging.