Accuracy of Dental Implant Placement Using Dynamic Navigation With Different Optical Tracking Systems: An In Vitro Study
Bibliographic record
Abstract
BACKGROUND: While dynamic navigation systems demonstrate superior precision in implant placement, the influence of variations in optical tracking technology on accuracy warrants further investigation. PURPOSE: To compare the accuracy of dynamic navigation for implant placement using active infrared (AI), passive infrared (PI), and passive blue light (PB) optical tracking technologies. The null hypothesis is that there is no significant difference between the three alternative optical tracking technologies. MATERIALS AND METHODS: Three surgeons placed implants on 10 models using AI, PI, and PB optical tracking systems. Implants were placed on two sites per model (mandibular central incisors and mandibular first molar), with 180 implants assigned to be placed into 90 mandible models. The planned and placed implant positions were superimposed to assess procedural accuracy. RESULTS: The mean coronal, apical, and axial deviations for all implants were 0.82 ± 0.02 mm, 0.92 ± 0.02 mm, and 1.56° ± 0.06°, respectively. In the mandibular left central incisors, the coronal, apical, and axial deviations of the PB and AI groups and the PB and PI groups were significantly different (p < 0.032, p < 0.001, p < 0.001, respectively, and both p < 0.001). In the mandibular left first molar, the coronal, apical, and axial deviations of the PB and PI groups were significantly different (p < 0.001, p < 0.001, p = 0.003, respectively). The coronal and apical deviations of the PB and AI groups exhibited statistically significant differences (both p < 0.001). CONCLUSIONS: The PB optical tracking system outperformed the AI and PI optical tracking systems regarding dynamic navigation accuracy, while the AI and PI optical tracking systems were comparable. All systems exhibit sufficient accuracy in vitro.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".