Modular Preoperative Planning Software for Computer‐Aided Oral Implantology and the Application of a Novel Stereolithographic Template: A Pilot Study
Bibliographic record
Abstract
PURPOSE: In the field of oral implantology, there is a trend toward computer-aided implant surgery, especially the application of computerized tomography (CT)-derived surgical templates. However, because of relatively unsatisfactory match between the templates and receptor sites, conventional surgical templates may not be accurate enough for the severely resorbed edentulous cases during the procedure of transferring the preoperative plan to the actual surgery. The purpose of this study is to introduce a novel bone-tooth-combined-supported surgical guide, which is designed by utilizing a special modular software and fabricated via stereolithography technique using both laser scanning and CT imaging, thus improving the fit accuracy and reliability. MATERIALS AND METHODS: A modular preoperative planning software was developed for computer-aided oral implantology. With the introduction of dynamic link libraries and some well-known free, open-source software libraries such as Visualization Toolkit (Kitware, Inc., New York, USA) and Insight Toolkit (Kitware, Inc.) a plug-in evolutive software architecture was established, allowing for expandability, accessibility, and maintainability in our system. To provide a link between the preoperative plan and the actual surgery, a novel bone-tooth-combined-supported surgical template was fabricated, utilizing laser scanning, image registration, and rapid prototyping. Clinical studies were conducted on four partially edentulous cases to make a comparison with the conventional bone-supported templates. RESULTS: The fixation was more stable than tooth-supported templates because laser scanning technology obtained detailed dentition information, which brought about the unique topography between the match surface of the templates and the adjacent teeth. The average distance deviations at the coronal and apical point of the implant were 0.66 mm (range: 0.3-1.2) and 0.86 mm (range: 0.4-1.2), and the average angle deviation was 1.84 degrees (range: 0.6-2.8 degrees ). CONCLUSIONS: This pilot study proves that the novel combined-supported templates are superior to the conventional ones. However, more clinical cases will be conducted to demonstrate their feasibility and reliability.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".