Abstract
PURPOSE: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.