Neutron microtomography to investigate the bone-implant interface—comparison with histological analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Bone properties and especially its microstructure around implants are crucial to evaluate the osseointegration of prostheses in orthopaedic, maxillofacial and dental surgeries. Given the intrinsic heterogeneous nature of the bone microstructure, an ideal probing tool to understand and quantify bone formation must be spatially resolved. X-ray imaging has often been employed, but is limited in the presence of metallic implants, where severe artifacts generally arise from the high attenuation of metals to x-rays. Neutron tomography has recently been proposed as a promising technique to study bone-implant interfaces, thanks to its lower interaction with metals. The aim of this study is to assess the potential of neutron tomography for the characterisation of bone tissue in the vicinity of a metallic implant. A standardised implant with a bone chamber was implanted in rabbit bone. Four specimens were imaged with neutron tomography and subsequently compared to non-decalcified histology to stain soft and mineralised bone tissues, used here as a ground-truth reference. An intensity-based image registration procedure was performed to place the 12 histological slices within the corresponding 3D neutron volume. Significant correlations ( p < 0.01) were obtained between the two modalities for the bone-implant contact ( BIC ) ratio ( R = 0.77) and the bone content inside the chamber ( R = 0.89). The results indicate that mineralised bone tissue can be reliably detected by neutron tomography. However, the BIC ratio and bone content were found to be overestimated with neutron imaging, which may be explained by its sensitivity to non-mineralised soft tissues, as revealed by histological staining. This study highlights the suitability of neutron tomography for the analysis of the bone-implant interface. Future work will focus on further distinguishing soft tissues from bone tissue, which could be aided by the adoption of contrast agents.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it