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Decision-Support for Restorative Dentistry: Hybrid Optimization Enhances Detection on Panoramic Radiographs.

Gül Ateş1, Fuat Türk2, Elif Tuba Akçın3

  • 1Department of Prosthodontics, Faculty of Dentistry, Yıldırım Beyazit University, 06800 Ankara, Turkey.

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A hybrid optimization-assisted approach using machine learning (ML) and deep learning (DL) showed improved performance for classifying dental restorations on panoramic radiographs. This AI tool acts as a decision support for dentists, not a standalone diagnostic system.

Keywords:
artificial intelligence (AI)convolutional neural network (CNN)dental restorationshybrid optimization (HGWO-PSO)panoramic dental radiographs

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Area of Science:

  • Dental Radiology
  • Artificial Intelligence in Dentistry
  • Machine Learning Applications

Background:

  • Artificial intelligence (AI) is increasingly utilized to enhance radiological assessments in dentistry.
  • Benchmarking machine learning (ML), deep learning (DL), and hybrid approaches is crucial for automated dental restoration classification.
  • Panoramic radiographs are a common imaging modality for evaluating dental restorations.

Purpose of the Study:

  • To benchmark ML, DL, and a hybrid optimization-assisted approach for automatic five-class image-level classification of dental restorations on panoramic radiographs.
  • To evaluate the performance of different AI models in identifying dental restorations, including fillings, implants, root canal treatments, fixed partial dentures/bridges, and crowns.

Main Methods:

  • Analysis of 353 anonymized panoramic images with 2137 labeled restorations.
  • Image preprocessing included cropping, histogram equalization, CLAHE, and GLCM texture feature extraction.
  • Evaluation of a three-stage pipeline: (i) GLCM features with ML/DL, (ii) Hybrid Grey Wolf-Particle Swarm Optimization (HGWO-PSO) with SVM, and (iii) Convolutional Neural Network (CNN) on raw images.

Main Results:

  • The HGWO-PSO + SVM configuration achieved the highest accuracy (73.15%), outperforming the CNN (68.52%) and traditional ML models (SVM 67.89%, DT 59.09%, RF 58.33%, K-NN 53.70%).
  • The hybrid approach demonstrated superior macro-precision, recall, and F1-scores (0.728) compared to other methods.
  • Performance was assessed using an 80/20 per-patient split and 5-fold cross-validation, confirming sufficient statistical power.

Conclusions:

  • The hybrid optimization-assisted classifier moderately improved detection performance on this single-center dataset compared to baseline CNN and conventional ML.
  • The proposed system serves as a decision-supportive tool for dentists, acknowledging limitations in dataset size and class imbalance.
  • Future research should focus on larger, multi-center datasets and advanced DL models to enhance generalizability and clinical utility.