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Related Experiment Video

Updated: Jul 8, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Dental image analysis and surgical decision support using modified generative adversarial networks.

G Sivasathiya1, R Thiagarajan2

  • 1Department of Information Technology, Ramco Institute of Technology, Rajapalayam, 626117, India. g.sivasathiya@gmail.com.

BMC Oral Health
|July 7, 2026
PubMed
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This study introduces a modified Generative Adversarial Network (mGAN) for enhanced dental surgical planning. The mGAN integrates domain knowledge and multimodal features, improving risk prediction and surgical decision-making for better patient outcomes.

Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Image Analysis
  • Deep Learning for Surgical Planning

Background:

  • Clinical dental care faces challenges in risk estimation due to limited integration of multimodal features and domain knowledge.
  • Existing analytical platforms struggle to incorporate clinical literature and evidence-based principles.
  • Deep learning offers potential for complex case analysis but requires structural adjustments for multimodal data.

Purpose of the Study:

  • To develop an innovative deep learning framework for improved dental surgical planning.
  • To enhance the integration of multimodal features and clinical domain knowledge in diagnostic platforms.
  • To create a system that supports clinical decision-making by providing uncertainty-sensitive predictions.

Main Methods:

Keywords:
IACLLM-derived knowledgeM3M roots and Surgical decision supportW-NetmGAN

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  • A modified Generative Adversarial Network (mGAN) incorporating a W-Net architecture (U-Net and V-Net combination) for multi-scale feature analysis.
  • Integration of Large Language Models (LLMs) for hyperparameter tuning and incorporating domain knowledge on mandibular anatomy and pathology.
  • Implementation of Probability Feature Fusion (PFF) for adaptive multi-modal feature integration and generating probabilistic maps for M3M root and IAC contact.

Main Results:

  • The mGAN achieved 95.7% Root Surface Area Accuracy (RSAA) and 0.925 Dice Similarity Coefficient (DSC) for anatomical segmentation.
  • Achieved 91.8% sensitivity and 96.7% specificity in classifying surgical necessity.
  • LLM integration improved risk correlation by 6.4%, and PFF further enhanced sensitivity by 1.9% over baseline models.
  • Processing time of 2-2.2 minutes per case using 1500 CBCT scans.

Conclusions:

  • The proposed mGAN effectively combines domain expertise with deep learning for dental surgical planning.
  • This approach offers a valuable tool for clinical decision-making, minimizing iatrogenic complications.
  • The system provides uncertainty-sensitive predictions and maintains computational efficiency, potentially enhancing treatment outcomes.