CT IMAGE REGISTRATION FOR EVALUATING REVERSE SHOULDER ARTHROPLASTY PLACEMENT
Bibliographic record
Abstract
Reverse shoulder arthroplasty has a high complication rate related to glenoid implant instability and screw loosening. Better radiographic post-operative evaluation may help in understanding complications causes. Medical radiographic imaging is the conventional technique for post-operative component placement analysis. Studies suggest that volumetric CT is better than use of CT slices or conventional radiographs. Currently, post-operative CT use is limited by metal-artifacts in images. This study evaluated inter-observer reliability of pre-operative and post-operative CT images registration to conventional approaches using radiographs and CT slices in measuring reverse shoulder arthroplasty glenoid implant and screw percentage in bone. Pre-operative and post-operative CT scans, and post-operative radiographs were obtained from six patients that had reverse shoulder arthroplasty. CT scans images were imported into a medical imaging processing software and each scapula, glenoid implant and inferior screw were reconstructed as 3D models. Post-operative 3D models were imported into the pre-operative reference frame and matched to the pre-operative scapula model using a paired-point and a surface registration. Measurements on registered CT models were done in reference to the pre-operative scapula model coordinate frame defined by a computer-assisted designed triad positioned in respect to the center of the glenoid fossa and trigonum scapulae (medial-lateral, z axis) and superior and inferior glenoid tubercle (superior-inferior, y axis). The orthogonal triad third axis defined the anterior-posterior axis (x axis). A duplicate triad was positioned along the central axis of the glenoid implant model. Using a virtual protractor, the glenoid implant inclination was measured from its central axis and the scapula transverse plane (x - z axes) and version from the coronal plane (y - z axes). Inferior screw percentage in bone was measured from a Boolean intersection operation between the pre-operative scapula model and the inferior screw model. For CT slices and radiographic measurements, a first 90-degree Cobb angle, from medical records software, was positioned from the trigonum scapulae to the centre of the central peg. Using the 90-degree line as reference, a second Cobb angle was drawn from the most superior to the most inferior point of the glenoid implant for inclination and from of the most anterior to the most posterior point for version. Version can only be measured using CT slices. Screw percentage in bone was calculated from screw length measures collected with a distance-measuring tool from the software. For testing the inter-observer reliability of the three methods, measures taken by three qualified observers were analysed using an intra-class correlation coefficient (ICC) method. The 3D registration method showed excellent reliability (ICC > 0.75) in glenoid implant inclination (0.97), version (0.98) and screw volume in bone (0.99). Conventional methods showed poor reliability (ICC This CT registration of post-operative to pre-operative novel method for quantitatively assessing reverse shoulder arthroplasty glenoid implant positioning and screw percentage in bone, showed excellent inter-observer reliability compared to conventional 2D approaches. It overcomes metal-artifact limitations of post-operative CT evaluation.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".