A Numerical Approach of Estimating Dental Implant Healing Process Using Resonance Frequency Analysis (RFA)
Bibliographic record
Abstract
This paper investigates primary stability of dental implant that indicates the process of bone-implant integration. This integration is known to happen at the boundary of the bone and dental implant contact surface. The resonance frequency of dental implant is used as the parameter for this investigation due to its high sensitivity to boundary condition variations. In this study, resonance frequency analysis (RFA) of the jaw-implant structure is carried out using finite element modeling. The FEM analyses are conducted in ANSYS modal analysis simulation environment. The FEM model of the structure includes titanium implant, Cancellous and cortical bone. Different implant-bone interface conditions are studied for this investigation. Various boundary conditions were studied to identify natural frequencies of jaw-implant structure. Our analysis shows that the resonance frequency of the implant increases during the healing period and reaches a plateau when the implant-bone interface was fully integrated. The results show that RFA could be suggested as a non-invasive, reliable and accurate diagnostic method for early assessment of the healing stages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".