#dental-imaging News & Analysis

5 articles tagged with #dental-imaging. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AINeutralarXiv – CS AI · Jun 95/10

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3D Oral Modelling with Improved Vertex Distribution Using Matching-Based Learning

Researchers improved a deep learning framework for 3D oral reconstruction by introducing Hungarian matching and Repulsion Loss to achieve more uniform vertex distribution across predicted dental models. While numerical accuracy decreased from 77.49% to 68.02%, the trade-off eliminates vertex clustering in sparse regions, producing more clinically useful reconstructions from intraoral images.

AINeutralarXiv – CS AI · Jun 56/10

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Deep Learning-based 3D Oral Cavity Reconstruction Using 2D Intraoral Images

Researchers propose a deep learning method that reconstructs 3D oral cavity models from just ten 2D intraoral images, eliminating the need for expensive scanning equipment or uncomfortable impression-taking procedures. Achieving 77.49% accuracy using MobileNetV2 and multi-head attention mechanisms, the approach offers a cost-effective alternative for dental modeling, though it currently exhibits uneven point distribution in reconstructed models.

AINeutralarXiv – CS AI · May 285/10

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Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction

Researchers have developed a gradient-step plug-and-play algorithm that uses a trained denoiser model to reduce photon noise in dental cone-beam CT reconstructions. The method combines inverse problem formulation with machine learning, demonstrating effective denoising on synthetic data and promising generalization to real-world dental imaging applications.

AIBullisharXiv – CS AI · Feb 276/104

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HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography

Researchers developed HARU-Net, a novel AI architecture for denoising cone-beam computed tomography (CBCT) medical images that outperforms existing state-of-the-art methods while using less computational resources. The system addresses critical noise issues in low-dose dental and maxillofacial imaging by combining hybrid attention mechanisms with residual U-Net architecture.

AINeutralarXiv – CS AI · Mar 34/104

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OPGAgent: An Agent for Auditable Dental Panoramic X-ray Interpretation

Researchers have developed OPGAgent, a multi-tool AI system for analyzing dental panoramic X-rays that outperforms current vision language models. The system uses specialized perception modules and a consensus mechanism to provide more accurate and auditable dental imaging interpretation across multiple diagnostic tasks.