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OralHybridNet: A Deep Learning Framework for Multi-Label Classification of Dental Restorations and Prostheses in

Zohaib Khurshid1, Ramy Moustafa Moustafa Ali1, Ali Sulaiman Alharbi1

  • 1Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia.

Inquiry : a Journal of Medical Care Organization, Provision and Financing
|April 13, 2026
PubMed
Summary

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This study introduces OralHybridNet, a deep learning model for automated dental condition prediction from panoramic radiographs. It achieves high accuracy, addressing challenges like rare diseases and class imbalance in dental imaging.

Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Automated dental condition prediction from Orthopantomogram (OPG) radiographs is challenging due to class imbalance, rare pathologies, and complex anatomy.
  • Existing methods struggle to effectively analyze the intricate details within OPG images for accurate diagnosis.

Purpose of the Study:

  • To develop and evaluate OralHybridNet, a novel hybrid deep learning framework for automated multi-label dental restoration classification.
  • To address the limitations of current automated dental diagnostic systems by integrating advanced deep learning techniques.

Main Methods:

  • A hybrid deep learning framework, OralHybridNet, was developed, integrating hierarchical convolutional neural networks with dual-attention mechanisms.
  • A multinational dataset of 2047 clinician-annotated OPGs across 7 diagnostic labels was utilized.
Keywords:
artificial intelligencedeep learningdental radiologyfeature fusionmulti-label classificationpanoramic radiography

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  • Adaptive augmentation, Hybrid Feature Selection (HFS), and a KNN Fine classifier were employed to enhance performance and reduce dimensionality.

    • Main Results:

    • OralHybridNet achieved 96.0% accuracy, 97.6% precision, and 0.993 AUC-ROC, outperforming ResNet50 baselines.
    • The framework demonstrated real-time inference capability with a 9ms processing time.
    • The KNN Fine classifier on fused features provided the highest diagnostic performance.

    Conclusions:

    • OralHybridNet presents a promising proof-of-concept for automated multi-label dental restoration classification.
    • The proposed framework effectively mitigates challenges in OPG analysis, offering high accuracy and efficiency.
    • This approach has the potential to significantly advance computer-aided diagnosis in dentistry.