Accuracy of Osteophyte Detection in Conventional Computed Tomography and Magnetic Resonance Imaging of Joints
Bibliographic record
Abstract
Osteophytes are bony spurs on normal bone that develop as an adaptive reparative process due to excessive stress at/near a joint. As osteophytes develop from normal bone, they are not always well depicted in common imaging techniques (e.g. CT, MRI). This creates a challenge for preoperative planning and image‐guided surgical methods that are commonly incorporated in the clinical routine of orthopedic surgery. The study examined the accuracy of osteophyte detection in clinical CT and MRI scans of varying types of joints. The investigation was performed on three fresh‐frozen ex‐vivo human resected joints identified as having a high potential for presentation of osteophytes. The specimens underwent varying imaging protocols for CT scanning and clinical protocols for MRI. After dissection of the joint, the specimens were subjected to structured 3D light scanning to establish a reference model of the anatomy. Scans from the imaging protocols were segmented and their 3D models were co‐registered to the light scanner models. The quality of the osteophyte images were evaluated by determining the Root Mean Square (RMS) error between the segmented osteophyte models and the light scan model. The mean RMS errors for CT and MRI scanning were 1.169 mm and 1.419 mm, respectively. Comparing the different CT parameters, significance was achieved with scanning at 120 kVp and 1.25 mm slice thickness to depict osteophytes; also, significance was also apparent at a lower voltage (100 kVp). Preliminary results demonstrate that osteophyte detection may be dependent on the degree of calcification of the osteophyte. They also illustrate that while some imaging parameters were more favorable than others, a more accurate osteophyte depiction may result from the combination of both MRI and CT scanning.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".