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Related Experiment Videos

From routine full-spine radiographs to decision-oriented Risser stratification: an interpretable deep-learning

Zexi Wang1, Yuan Zhang2, Yixi Wang1

  • 1Department of Minimally Invasive Spine and Precision Orthopedics, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.

Frontiers in Pediatrics
|July 2, 2026
PubMed
Summary

Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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A deep-learning model accurately stratifies skeletal maturity in adolescent idiopathic scoliosis (AIS) using radiographs, improving efficiency and aiding treatment planning. This AI tool enhances Risser staging for better growth potential assessment.

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Orthopedics and Spine Surgery

Background:

  • Accurate assessment of remaining growth is critical for risk stratification and treatment planning in adolescent idiopathic scoliosis (AIS).
  • Risser staging, a common method, exhibits moderate reproducibility in clinical practice, especially on standard radiographs where key features are small.
  • This limitation highlights the need for more objective and reproducible methods for skeletal maturity assessment in AIS.

Purpose of the Study:

  • To develop and evaluate an interpretable deep-learning model for automatic skeletal maturity stratification in AIS.
  • The model aims to classify patients into Risser stages 0-2 versus 3-5 using routine full-spine radiographs.
  • To assess the model's interpretability and its impact on clinical readers' efficiency and agreement.
Keywords:
Grad-CAMResNet-18adolescent idiopathic scoliosis (AIS)binary classificationclinical decision supportdeep learningrisser stagingskeletal maturity assessment

Related Experiment Videos

Main Methods:

  • A retrospective study utilized 875 standing posteroanterior full-spine radiographs from AIS patients (aged 10-18 years).
  • An expert consensus standard was established, and a deep learning model (ResNet-18) was trained on automatically extracted pelvic regions.
  • Model performance was evaluated using AUC and Cohen's kappa, with interpretability assessed via Grad-CAM; a reader study compared unaided vs. model-assisted readings.

Main Results:

  • The deep-learning model achieved high performance for binary stratification (Risser 0-2 vs. 3-5) with an AUC of 0.938 and accuracy of 0.875.
  • Gradient-weighted class activation mapping (Grad-CAM) confirmed the model focused on relevant iliac apophysis ossification regions.
  • Model assistance significantly reduced reading time (by 9.7-11.8 seconds per case) and improved reader agreement with expert consensus, particularly for junior surgeons.

Conclusions:

  • An interpretable deep-learning model can effectively perform Risser stratification using routine radiographs, aiding clinical decision-making in AIS.
  • The AI tool demonstrated improved reading efficiency and accuracy, supporting standardized assessment of growth potential without additional imaging.
  • This approach holds promise for enhancing routine management of adolescent idiopathic scoliosis.