Spatial and volumetric validation of 3D ultrasound musculoskeletal system
Bibliographic record
Abstract
Synovial inflammation in the joint has previously been found to be positively correlated to pain experienced by patients suffering from osteoarthritis (OA);1 however, its role mediating cartilage degradation has increasingly become of interest. Monitoring inflammatory and structural changes in joints affected by OA is an important strategy to strengthen our understanding of disease progression, as well as to identify varying phenotypic presentations of the disease. Our lab has developed a 3D US musculoskeletal imaging system for quantitative assessment of synovial inflammation in OA. The system is equipped with a mechatronic counterbalanced arm that supports a 3D US-based linear motorized scanner and allows tracking of the 3D US scanner. This capability provides a method to fuse multiple acquired 3D US images into a larger view of the anatomy, which is needed to image the knee joint. The goal of this work was to evaluate (1) the accuracy and precision of the mechatronic arm’s tracking and (2) the accuracy of the volumetric measurements in the fused 3D US images. In the first experiment, we imaged an agar-based phantom with 5 embedded small spherical inclusions from varying positions. Using these tracked images, we measured the coordinates of the inclusions and calculated the target registration error (TRE) from the mean position of each inclusion. In the second experiment, 3D US images were acquired of an agar phantom containing a semi-cylindrical inclusion, which were fused. The inclusion was then manually segmented and the error between the segmented and actual volumes was determined. TREs for each coordinate component of the spherical inclusions were small, indicating accurate spatial tracking by the system. The average error of the segmented semi-cylindrical inclusion was 4.76%, demonstrating accurate fusion. Our results shows that the system is a promising tool for quantitative monitoring of soft-tissue changes in joints afflicted with musculoskeletal pathologies.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".