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Related Experiment Video

Updated: May 31, 2026

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PRAD++: Towards Robust Periapical Radiograph Analysis through Dataset and Model Advancements.

Zhenhuan Zhou, Yuchen Zhang, Peng Wang

    IEEE Transactions on Medical Imaging
    |May 29, 2026
    PubMed
    Summary
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    This study introduces PRAD++, a large dataset for dental periapical radiograph analysis, and PRNet++, a deep learning model that achieves state-of-the-art performance in segmentation and classification tasks.

    Area of Science:

    • Dentistry
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Deep learning (DL) models require large annotated datasets, but high-quality periapical radiograph (PR) datasets are scarce due to annotation costs and image quality issues.
    • PRs are crucial in endodontics, yet limited data hinders DL development for PR analysis.
    • Existing DL models struggle with the complexities of PR interpretation.

    Purpose of the Study:

    • To address the scarcity of annotated PR data by introducing PRAD++, a large-scale dataset.
    • To develop an advanced DL model, PRNet++, for end-to-end PR analysis.
    • To improve the accuracy, robustness, and clinical interpretability of DL models in PR analysis.

    Main Methods:

    • Created PRAD++, a dataset with 10,000 PR images featuring 9 pixel-level segmentation categories and 17 image-level classification labels, annotated by clinical experts.

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  • Developed PRNet++, an end-to-end network utilizing Multi-scale Wavelet Convolution (MWCN) and Channel Fusion Attention (CFA) for multi-scale feature integration.
  • Incorporated an Expert Prior Injection (EPI) loss to integrate domain-specific dental knowledge, linking segmentation to classification for enhanced accuracy.

    • Main Results:

    • PRNet++ achieved an average Dice Similarity Coefficient (DSC) of 81.25% for segmentation on the PRAD++ dataset.
    • The model obtained macro- and micro-averaged PR-AUCs of 66.58% and 79.10% for classification, outperforming state-of-the-art methods.
    • PRNet++ demonstrated superior robustness and interpretability, particularly in challenging clinical categories, validated by ablation and visualization studies.

    Conclusions:

    • PRAD++ and PRNet++ provide a valuable resource and a powerful tool for advancing DL-based dental image analysis, specifically for periapical radiographs.
    • The proposed PRNet++ architecture, with its MWCN, CFA, and EPI loss, effectively addresses the limitations of existing models.
    • The results highlight the potential of integrating expert knowledge into DL models for improved clinical applicability and interpretability in dentistry.