Influence of exposure of customized dental implant abutments to different cleaning procedures: an in vitro study using AI-assisted SEM/EDS analysis.
CAD/CAM
Cleaning methods
Contamination
Disinfection
Energy-dispersive X-ray spectroscopy
Machine learning
SEM–EDS analysis
Scanning electron microscopy
Segmentation
Two-piece abutment
Journal
International journal of implant dentistry
ISSN: 2198-4034
Titre abrégé: Int J Implant Dent
Pays: Germany
ID NLM: 101676532
Informations de publication
Date de publication:
20 09 2023
20 09 2023
Historique:
received:
13
03
2023
accepted:
05
09
2023
medline:
22
9
2023
pubmed:
21
9
2023
entrez:
21
9
2023
Statut:
epublish
Résumé
Dental implant abutments are defined as medical devices by their intended use. Surfaces of custom-made CAD/CAM two-piece abutments may become contaminated during the manufacturing process in the dental lab. Inadequate reprocessing prior to patient care may contribute to implant-associated complications. Risk-adapted hygiene management is required to meet the requirements for medical devices. A total of 49 CAD/CAM-manufactured zirconia copings were bonded to prefabricated titanium bases. One group was bonded, polished, and cleaned separately in dental laboratories throughout Germany (LA). Another group was left untreated (NC). Five groups received the following hygiene regimen: three-stage ultrasonic cleaning (CP and FP), steam (SC), argon-oxygen plasma (PL), and simple ultrasonic cleaning (UD). Contaminants were detected using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) and segmented and quantified using interactive machine learning (ML) and thresholding (SW). The data were statistically analysed using non-parametric tests (Kruskal-Wallis test, Dunn's test). Significant differences in contamination levels with the different cleaning procedures were found (p ≤ 0.01). The FP-NC/LA groups showed the most significant difference in contamination levels for both measurement methods (ML, SW), followed by CP-LA/NC and UD-LA/NC for SW and CP-LA/NC and PL-LA/NC for ML (p ≤ 0.05). EDS revealed organic contamination in all specimens; traces of aluminum, silicon, and calcium were detected. Chemothermal cleaning methods based on ultrasound and argon-oxygen plasma effectively removed process-related contamination from zirconia surfaces. Machine learning is a promising assessment tool for quantifying and monitoring external contamination on zirconia abutments.
Identifiants
pubmed: 37730937
doi: 10.1186/s40729-023-00498-8
pii: 10.1186/s40729-023-00498-8
pmc: PMC10511398
doi:
Substances chimiques
zirconium oxide
S38N85C5G0
Argon
67XQY1V3KH
Dental Implants
0
Oxygen
S88TT14065
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
33Informations de copyright
© 2023. Deutsche Gesellschaft für Implantologie im Zahn‐, Mund‐ und Kieferbereich e.V., Japanese Society of Oral Implantology.
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