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Peri-Implantitis

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Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes.

Theranostics
The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. We investigated a unique cohort of peri-implantitis patie...

Predictive modeling for peri-implantitis by using machine learning techniques.

Scientific reports
The purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 1...

DEEP LEARNING-DRIVEN SEGMENTATION OF DENTAL IMPLANTS AND PERI-IMPLANTITIS DETECTION IN ORTHOPANTOMOGRAPHS: A NOVEL DIAGNOSTIC TOOL.

The journal of evidence-based dental practice
INTRODUCTION AND OBJECTIVE: Dental implants are well-established for restoring partial or complete tooth loss, with osseointegration being essential for their long-term success. Peri-implantitis, marked by inflammation and bone loss, compromises impl...

Comprehensive Analysis of Immune Infiltration and Key Genes in Peri-Implantitis Using Bioinformatics and Molecular Biology Approaches.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Peri-implantitis is the main cause of failure of implant treatment, and there is little research on its molecular mechanism. This study aimed to identify key biomarkers and immune infiltration of peri-implantitis using a bioinformatics met...

Artificial intelligence for dental implant classification and peri-implant pathology identification in 2D radiographs: A systematic review.

Journal of dentistry
OBJECTIVE: This systematic review aimed to summarize and evaluate the available information regarding the performance of artificial intelligence on dental implant classification and peri-implant pathology identification in 2D radiographs.