Potential contribution of periapical radiographic film image processing for forensic identification
Bibliographic record
Abstract
ABSTRACT Periapical X-rays are the most common complementary tests in the dental clinic. The indication of image tests in forensic identification depends on the produced X-rays quality. The image processing of conventional radiographs can improve image quality. This study aimed to report the potential contribution of image processing from radiographic films by digitally edited periapical radiographs for case reporting of positive identification. The results of anthropological examinations and dental arches of the victim matched the information transferred by the family of the missing person. The antemortem and postmortem periapical radiographs were digitized on photo scanner (Hewlett-Packard Development Company, HP ScanJet G4050 Photo, United States) and images were processed in Corel PaintShop Pro X4 editing software (Corel Corporation, v14, Canada). The comparison of antemortem and postmortem periapical radiographs digital images allowed to determine 8 concordant points in the contour and delimit the maxillary sinus as well as periodontal and dental structures of the tooth 17. Identification of the individual was possible by digital editing of radiographs in computer software. Editing allowed adjusting image brightness, contrast and sharpness, color temperature and saturation of tooth-jaw structures. Such technological feature effectively contributed to positive identification performed by Forensic Dentistry.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".