Results 171 to 180 of about 13,768 (296)

Artificial Intelligence in Periodontology: A Systematic Review

open access: yesJournal of Periodontal Research, EarlyView.
AI shows promise across periodontology, with deep learning achieving strong performance for image‐based diagnosis of periodontitis. However, limited data diversity, inconsistent metrics, and scarce external validation raise concerns about generalizability and clinical applicability.
Antonin Tichy   +7 more
wiley   +1 more source

Peri‐Implantitis and Periodontitis: Biological Convergence, Contextual Divergence

open access: yesJournal of Periodontal Research, EarlyView.
Periodontal and peri‐implant tissues differ and coincide in many aspects, from the clinical and radiological perspective, including histology, microbiology, and molecular markers. Thus, health and disease may also follow different and similar routes.
Pablo Galindo‐Moreno   +6 more
wiley   +1 more source

Platelet‐Rich Fibrins as Local Drug‐Delivery Carriers

open access: yesJournal of Periodontal Research, EarlyView.
Autologous platelet concentrates (APCs), particularly platelet‐rich fibrin (PRF), act as biologically active fibrin scaffolds, capable of entrapping and gradually releasing therapeutic agents in oral and periodontal therapy. The incorporation of bioactive compounds, such as antibiotics, antifungals, vitamins, antidiabetic drugs, and exosomes, enhances ...
Karim M. Fawzy El‐Sayed   +1 more
wiley   +1 more source

M1 and M2 macrophages markers are alternately expressed during periapical lesion development. [PDF]

open access: yesJ Appl Oral Sci
Pucinelli CM   +8 more
europepmc   +1 more source

Management of periapical lesion with persistent exsudate. [PDF]

open access: yesBraz Dent J, 2022
Quaresma SA   +7 more
europepmc   +1 more source

Sector Classification of Unerupted Maxillary Canines: A Deep Learning‐Based Automated Framework Using Panoramic Radiographs

open access: yesOrthodontics &Craniofacial Research, EarlyView.
ABSTRACT Objectives To develop a deep learning‐based framework to automate sector classification of unerupted maxillary canines (UMCs), assessing its accuracy and reliability compared to human ones. Material and Methods One thousand five hundred twenty‐eight UMCs from digital panoramic radiographs (PRs) were selected using data from the Dental ...
Marzio Galdi   +7 more
wiley   +1 more source

Deep Learning-Based Periapical Lesion Detection on Panoramic Radiographs. [PDF]

open access: yesDiagnostics (Basel)
Szabó V   +7 more
europepmc   +1 more source

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