Determination of inferior alveolar nerve position via anatomical dissection and micro‐CT: A view towards dental implants
Bibliographic record
Abstract
Hundreds of thousands of dental implants are performed each year. Precise knowledge of inferior alveolar nerve (IAN) location prior to dental implant placement is necessary to avoid complications of nerve damage such as sensory disturbances. The IAN courses anteriorly within the body of the mandible providing sensory nerve supply to the mandibular teeth. Current imaging modalities used to visualize the position of the IAN prior to dental implant placement may not be accurate. This study provides new information for locating the nerve more accurately. Micro‐CT images were acquired at a slice thickness of 154μm from 8 cadaveric mandibles. The same specimens were bisected and each hemi‐mandible was divided into 5 sections according to pre‐defined molar and pre‐molar width measurements. Superior, inferior, buccal and lingual bone distance measurements surrounding the mandibular canal were obtained by direct digital caliper measurements, and compared with corresponding μCT measurements obtained using the AMIRA segmentation and measuring software. Preliminary results suggest substantial variability in bone distance superior to the mandibular canal depending on the length of time since tooth loss. This finding highlights the importance of using an imaging modality that provides the highest spatial accuracy, when determining IAN location during the pre‐implantation stage of dental implant placement. Grant Funding Source : Internal
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".