Three‐Dimensional Magnetic Resonance Imaging Quantification of Glenoid Bone Loss Is Equivalent to 3‐Dimensional Computed Tomography Quantification: Cadaveric Study
Bibliographic record
Abstract
PURPOSE: To assess the ability of 3-dimensional (3D) magnetic resonance imaging (MRI, 1.5 and 3 tesla [T]) to quantify glenoid bone loss in a cadaveric model compared with the current gold standard, 3D computed tomography (CT). METHODS: Six cadaveric shoulders were used to create a bone loss model, leaving the surrounding soft tissues intact. The anteroposterior (AP) dimension of the glenoid was measured at the glenoid equator and after soft tissue layer closure the specimen underwent scanning (CT, 1.5-T MRI, and 3-T MRI) with the following methods (0%, 10%, and 25% defect by area). Raw axial data from the scans were segmented using manual mask manipulation for bone and reconstructed using Mimics software to obtain a 3D en face glenoid view. Using calibrated Digital Imaging and Communications in Medicine images, the diameter of the glenoid at the equator and the area of the glenoid defect was measured on all imaging modalities. RESULTS: In specimens with 10% or 25% defects, no difference was detected between imaging modalities when comparing the measured defect size (10% defect P = .27, 25% defect P = .73). All 3 modalities demonstrated a strong correlation with the actual defect size (CT, ρ = .97; 1.5-T MRI, ρ = .93; 3-T MRI, ρ = .92, P < .0001). When looking at the absolute difference between the actual and measured defect area, no significance was noted between imaging modalities (10% defect P = .34, 25% defect P = .47). The error of 3-T 3D MRI increased with increasing defect size (P = .02). CONCLUSIONS: Both 1.5- and 3-T-based 3D MRI reconstructions of glenoid bone loss correlate with measurements from 3D CT scan data and actual defect size in a cadaveric model. Regardless of imaging modality, the error in bone loss measurement tends to increase with increased defect size. Use of 3D MRI in the setting of shoulder instability could obviate the need for CT scans. CLINICAL RELEVANCE: The goal of our work was to develop a reproducible method of determining glenoid bone loss from 3D MRI data and hence eliminate the need for CT scans in this setting. This will lead to decreased cost of care as well as decreased radiation exposure to patients. The long-term goal is a fully automated system that is as approachable for clinicians as current 3D CT technology.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".