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Updated: Jan 17, 2026

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Intervertebral disc anomaly intelligent classification system based on deep learning, IDAICS.

Zhiheng Gao1, Yuchen Qian1, Rongkang Fan1

  • 1Department of Orthopaedics, The First Affiliated Hospital of Soochow University, Suzhou, China.

Frontiers in Radiology
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning model accurately classifies intervertebral disc abnormalities like degeneration and herniation. This automated approach improves diagnostic efficiency and aids in better spinal health management.

Keywords:
CTYOLOv8-segautomated diagnosisclassification accuracydeep learningintervertebral disc abnormalitiesspinal health

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Area of Science:

  • Spinal imaging analysis
  • Artificial intelligence in medicine
  • Deep learning for medical diagnosis

Background:

  • Intervertebral disc anomalies (degeneration, herniation) are leading causes of chronic spinal pain and disability.
  • Manual analysis of spinal images is subjective and time-consuming.
  • Deep learning offers a promising avenue for automated and precise classification of disc abnormalities.

Purpose of the Study:

  • To develop and evaluate a deep learning-based method for classifying intervertebral disc abnormalities.
  • To enhance diagnostic accuracy and clinical efficiency in spinal health management.

Main Methods:

  • A dataset of 574 CT images of intervertebral discs was collected and labeled (normal, Schmorl's nodes, disc bulge, disc protrusion).
  • A YOLOv8-seg network was utilized for classification, with data preprocessing applied.
  • The dataset was split into 500 training and 74 validation images.

Main Results:

  • The developed system (IDAICS) achieved a classification accuracy exceeding 93.2% for various intervertebral disc anomalies.
  • A high kappa coefficient of 0.905 (P < 0.001) indicates strong agreement and reliability.
  • The model demonstrated high accuracy in identifying disc degeneration, herniation, and bulging.

Conclusions:

  • Deep learning provides an efficient, reliable alternative to manual assessment for intervertebral disc abnormalities.
  • Automated diagnosis using this method can significantly improve clinical decision-making.
  • The approach holds substantial potential for enhancing overall spinal health management outcomes.