Imaging Technique Selection for the Preoperative Planning of Oral Implants: A Review of the Literature
Bibliographic record
Abstract
BACKGROUND: As the use of oral implants for the treatment of partially as well as fully edentulous patients has increased the past two decades, more specialized radiographic techniques have become available for the preoperative planning of oral implant placement. PURPOSE: The goal of this article was to enable clinicians to select the appropriate imaging techniques when planning for oral implants. MATERIALS AND METHODS: This artide reviews the available literature about various imaging techniques and their indication for the preoperative planning of oral implants. The advantages and drawbacks of each technique are described. A dosimetric overview is given relative to different radiologic techniques used in various clinical situations. RESULTS: For preoperative planning of implant placement, advantages and drawbacks of the available imaging techniques have been considered, which allows guidelines for image technique selection to be formulated based on the clinical situation provided, considering the diagnostic yield of each technique and the radiation doses involved. CONCLUSIONS: From the available literature, it can be stated that many clinical situations demand the use of cross-sectional imaging techniques for optimal preoperative planning of implant placement. Nevertheless, such techniques are not required in cases in which the clinical examination reveals sufficient bone width and where standard radiographic examinations, such as intraoral and panoramic radiography, reveal adequate bone height and space for implant placement.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".