Use of dental CBCT software for evaluation of medical CT‐acquired images in a multiple fatality incident: Proof of principles
Bibliographic record
Abstract
Comparison of post-mortem dental findings to ante mortem dental records is a well-established, frequently used scientific means of human identification. Dentistry has adapted a form of CT scanning that uses a cone-shaped beam and is thus termed cone beam computed tomography (CBCT). CBCT is presently being used in many aspects of dentistry including osseointegrated implant planning, orthodontics, endodontics, investigation of pathology, and assessment prior to complex dental extractions. The identification of seven individuals from multiple fatality incident was undertaken using a simple technique for completing comparative radiographic dental identifications using post-mortem medical computed tomographic (CT) image-acquisition techniques and commercially available dental software normally used in clinical care. The authors will show the means by which the harvesting of anatomically important data from medical CTs and conversion of these files was undertaken to provide crisp, clear post-mortem dental images for forensic comparison to assist in the identification of two adults and provide age stratification of three juveniles. The use of this technique has shown to be beneficial for expediting efficient identification of deceased individuals, helping to isolate which cases may need additional scientific methods of identification, saving time and money to the organization and eliminating biological/body substance or radiation exposure to the operator. The application of this software for use in forensic dental identification cases is presented, and the methodology to create post-mortem images suitable for comparison is detailed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".