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
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".