Marginal Bone Level around Implants Assessed in Digital and Film Radiographs: In Vivo Study in the Dog
Bibliographic record
Abstract
BACKGROUND: One of the objectives of postoperative radiographic examinations of implants is to evaluate the marginal bone height and its changes over time. PURPOSE: The purpose of this study was to assess the influence of digital image processing on measurements of the marginal bone level around implants. MATERIAL AND METHODS: Implants in beagle dogs, used to study the development of peri-implantitis and subsequent healing following treatment, were monitored with conventional radiography and a digital image plate system. Five observers measured the distance between a reference point and the bone level. Measurements in conventional radiographs were made with the use of an x-ray viewer (2x) and a magnifying lens (7x). For the digital images, the system's built-in measuring function was used together with five image processing techniques: edge enhancement (matrixes set on 5 and 25), inverted grey scale, single color highlight, and color intensity mapping together with the brightness and contrast control. From the time of maximum breakdown and the end of the experiment, histologic values were available. RESULTS: Differences between techniques and observers increased toward the end of the healing period. Measurements made at maximum breakdown did not differ significantly from the histologic value. Measurements made after healing all methods, except that using edge enhancement and a 25 x 25 kernel, differed significantly from the histologic value by underestimating the bone level. CONCLUSIONS: Measurements of bone level around implants from digital radiographs are as accurate and precise as those from film images. In particular cases, the use of specific image processing algorithms may improve both accuracy and precision. After healing, the histologic specimens showed an incomplete bone fill in the crater with a remaining thin layer of connective tissue in contact with the fixture, and in such situations, the morphology of the bone will give a more complicated diagnostic task.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".