Influence of exposure of customized dental implant abutments to different cleaning procedures: an in vitro study using AI-assisted SEM/EDS analysis

International Journal of Implant Dentistry

Research

Paul Hofmann¹  Franziska Schmidt²  Florian Beuer²  Dirk Duddeck³  Andreas Kunz⁴ 

[1] Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197, Berlin, Germany;Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197, Berlin, Germany;Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197, Berlin, Germany;Research Department, CleanImplant Foundation, Pariser Platz 4a, 10117, Berlin, Germany;Private Dental Laboratory, Schumannstraße 1, 10117, Berlin, Germany;

关键词: Two-piece abutment;    CAD/CAM;    Contamination;    Cleaning methods;    Scanning electron microscopy;    Energy-dispersive X-ray spectroscopy;    SEM–EDS analysis;    Disinfection;    Machine learning;    Segmentation;   

DOI  :  10.1186/s40729-023-00498-8

 received in 2023-03-13, accepted in 2023-09-05,  发布年份 2023

来源: Springer

【 摘 要 】

PurposeDental 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.MethodsA 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).ResultsSignificant 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.ConclusionsChemothermal 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.Graphical Abstract

【 授权许可】

CC BY   
© Deutsche Gesellschaft für Implantologie im Zahn‐, Mund‐ und Kieferbereich e.V., Japanese Society of Oral Implantology 2023

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