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Growing Science » Engineering Solid Mechanics » Developing an artificial neural network-based tool to predict roughness parameters and cellular viability on surfaces of dental implant fixtures treated with the SLA+Anodizing method

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Engineering Solid Mechanics
ISSN 2291-8752 (Online) - ISSN 2291-8744 (Print)
Quarterly Publication
Volume 13 Issue 2 pp. 217-228, 2025

Developing an artificial neural network-based tool to predict roughness parameters and cellular viability on surfaces of dental implant fixtures treated with the SLA+Anodizing method Pages 217-228 Right click to download the paper Download PDF

Authors: Ehsan Anbarzadeh, Bijan Mohammadi

📋 Author Affiliations:
E. Anbarzadeh1, B. Mohammadi ORCID 2
1 School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
2 School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran, Centre for Industrial Mechanics, Department of Mechanical and Electrical Engineering, Faculty of Engineering, University of Southern Denmark, Denmark, Iran
doi 10.5267/j.esm.2024.12.003
1 Source: Scopus
Crossref 1 Source: CrossRef

🔑 Keywords:

Abstract: This research pioneers the development of an innovative approach for refining dental implant fixture surfaces using the SLA+Anodizing method. Leveraging a rich dataset encompassing 68 distinct implant surface treatment states, the study employs an Artificial Neural Network (ANN) to predict crucial parameters such as surface roughness and cellular viability. Through meticulous training and validation, the ANN demonstrates a remarkable 3% error rate in comparison to experimental results, underscoring its precision. The methodology extends beyond prediction, facilitating the optimization of implant surfaces for enhanced osseointegration. Experimental validation, including Atomic Force Microscopy and Molecular Cytotoxicity Tests, corroborates the accuracy of the ANN predictions. The study pioneers a transformative era in dental implantology, introducing a tailored and adaptable approach that bridges gaps in understanding the intricate interplay between surface modifications and biological responses. This work sets the stage for a paradigm shift in dental science, emphasizing precision, personalization, and elevated standards of care.

How to cite this paper
APA: Anbarzadeh, E & Mohammadi, B. (2025). Developing an artificial neural network-based tool to predict roughness parameters and cellular viability on surfaces of dental implant fixtures treated with the SLA+Anodizing method. Engineering Solid Mechanics, 13(2), 217-228.
Chicago/Turabian: Anbarzadeh, E & Mohammadi, B. 2025. "Developing an artificial neural network-based tool to predict roughness parameters and cellular viability on surfaces of dental implant fixtures treated with the SLA+Anodizing method." Engineering Solid Mechanics 13, no. 2 (2025): 217-228.
AMA: Anbarzadeh, E & Mohammadi, B. Developing an artificial neural network-based tool to predict roughness parameters and cellular viability on surfaces of dental implant fixtures treated with the SLA+Anodizing method. Engineering Solid Mechanics. 2025;13(2):217-228.

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📚 Journal: Engineering Solid Mechanics | 📅 Year: 2025 | 📖 Volume: 13 | 📄 Issue: 2 | 👁️ Views: 509 | 📊 Crossref: 1

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