ArticleDiagnostics (Basel, Switzerland)2024
The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Novel marker-based cone-beam CT registration for enhanced accuracy in dental implant planning.Dento maxillo facial radiology · 2026Article
- Artificial Intelligence and Digital Workflow in Craniofacial Bone Tissue Engineering: From Cone-Beam Computed Tomography (CBCT) to Personalized Bioceramic Implants.Dentistry journal · 2026Review
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Deep learning-based automated assessment of alveolar bone loss in CBCT for periodontitis.Clinical oral investigations · 2026Article
- Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis.Biomedical engineering online · 2026Article
- Artificial Intelligence Models for the Detection and Quantification of Orthodontically Induced Root Resorption Using Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis.Dentistry journal · 2026Review
- Enhancing Image Quality in Dental-Maxillofacial CBCT: The Impact of Iterative Reconstruction and AI on Noise Reduction-A Systematic Review.Journal of clinical medicine · 2025Review
- Radiographic Evaluation of Implant Stability and Osseointegration in Adult Orthodontic Patients : Radiographic Evaluation of Implant Stability and Osseointegration.Galen medical journal · 2025Review
- Radiographic Identification of Foreign Bodies in the Maxillofacial Region: A Case Report and Review for Aesthetic Surgery Planning.Case reports in radiology · 2025Article
- Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
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Authors and funding
5 authors.
Funding
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Abstract
objectiveThis study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. MATERIALS AND
methodsThis retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with the ClariCT.AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales.
resultsCompared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (
conclusionsAI-based noise reduction using ClariCT.AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.
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