ArUco‐based stylus reliability for reproductible 3D digitalisation of shoulder cartilage contours
Bibliographic record
Abstract
Imaging techniques in anatomy have developed rapidly over the last decades through the emergence of various 3D scanning systems. Depending on the dissection level, non-contact or tactile contact methods can be applied on the targeted structure. The aim of this study was to assess the inter and intra-observer reproducibility of an ArUco-based localisation stylus, that is, a manual technique on a hand-held stylus. Ten fresh-frozen, unembalmed adult arms were used to digitalise the glenoid cartilage related to the glenohumeral joint and the contour of the clavicle cartilage related to the acromioclavicular joint. Three operators performed consecutive digitalisations of each cartilage contour using an ArUco-based localisation stylus recorded by a single monocular camera. The shape of each cartilage was defined by nine shape parameters. Intra-observer repeatability and inter-observer reproducibility were computed using an intra-class correlation (ICC) for each of these parameters. Overall, 35.2 ± 2.4 s and 26.6 ± 10.2 s were required by each examiner to digitalise the contour of a glenoid and acromioclavicular cartilage, respectively. For most parameters, good-to-excellent agreements were observed concerning intra-observer (ICC ranging between 0.81 and 1.00) and inter-observer (ICC ranging between 0.75 and 0.99) reproducibility. To conclude, through a fast and versatile process, the use of an ArUco-based localisation stylus can be a reliable low-cost alternative to conventional imaging methods to digitalise shoulder cartilage contours.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".