Short Communication: Use of a Diagnostic Software to Predict Bone Density and Implant Stability in Preoperative CTs
Bibliographic record
Abstract
BACKGROUND: Computerized tomographs (CTs) are commonly used for presurgical planning of dental implant placement. It is possible that implant stability can be predicted based on quantitative measurements of bone density at planned implants sites with the use of diagnostic software. PURPOSE: The aim was to evaluate if there is a correlation between bone density measurements in specific implant positions in preoperative CTs and insertion torque (IT) and implant stability measurements when placing the implants. MATERIALS AND METHODS: The study comprised of four patients in whom presurgical CTs had been used to plan implant treatment. A total of 26 implants (Neoss, Harrogate, UK) were placed in the totally edentulous maxilla (n=3) or mandible (n=1). IT was measured during implant insertion and the torque/time curves examined for mean IT (newton-centimeters) over the total curve. The stability of implants was measured with resonance frequency analysis. The positions of the implants were extracted from a postoperative CT to the preoperative one. Bone density was measured with a dedicated software (3Diagnosys™ 3.0, 3Diemme, Cantù, Italy) in virtual hollow probes, indicating the bone volume within 1mm from each implant surface. The Spearman Rank correlation test was used to find possible correlations. RESULTS: Statistically significant correlations were found between mean bone density, mean IT, and implant stability measurements. CONCLUSIONS: This pilot study showed a correlation between bone density, as measured in Hounsfield units in preoperative CTs, and IT and implant stability measurements at the surgical placement of the implants. The findings support the idea that integration of bone density measurements in implant probes in preoperative CTs using treatment-planning software may be a useful feature to predict implant stability and to avoid failures.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".