ALGORITHMIC DETECTION OF THE BICIPITAL GROOVE IN 3D MODELS OF THE HUMERUS
Bibliographic record
Abstract
Patient specific 3D printed guides for shoulder arthroplasty exist for the glenoid but currently no commercially available system exists for the humerus. A primary barrier to adoption is the process of designing a custom guide for each patient that references specific alignment landmarks. The humerus bicipital groove is often used as a reference during the implantation of a shoulder prosthesis to guide the recreation of native retroversion. [1] It has also been used as a fixation landmark for patient specific instrumentation that guides the humeral osteotomy.[2] Therefore, an automated algorithm that identifies the bicipital groove would prove useful in the implementation of humeral component 3D printed patient specific guides. Segmented DICOM images from 39 cadaveric shoulders were used to generate 3D models of the humerus. The bicipital groove was manually digitized on all 39 models. Both the canal, and trans-epicondylar axes were automatically identified using in-house software. An anatomic coordinate system was then generated using the canal (axial) axis, the trans-epicondylar (medial-lateral) axis, and their cross-product as the third (anterior-posterior) axis. Following this, 2D axial slices of the humeral head were generated, and the perimeter was then unwound into polar coordinates with the centroid located along the canal axis. A weighted average of each polar perimeter was taken, and any cysts were identified as regions where the line doubles back onto itself and removed. The second derivative of the weighted average was then calculated to permit identification of radial depressions, and the bicipital groove location was estimated using an optimized combination of the following relationships regarding the axial trace: (i) the maximum radial mean lies between the endpoints of the articular surface, (ii) 2 of the 3 largest peaks of the 2nd derivative of the radial mean are endpoints of the articular surface which contain the maximum radial mean between them, and (iii) the minimum radial mean is highly correlated to bicipital grove location. Metrics are shown in Fiigure 1. Following optimization, the line of best fit for the estimated bicipital groove location was compared to the manually digitized locations using singular value decomposition yielding the first principal component direction. In the medial-lateral and anterior-posterior directions the difference between algorithm estimate and manually digitized line of best fit midpoints was −0.38+-0.97 mm (p=0.73) and 0.02+-0.96 mm (p=0.99), respectively for all 39 humeri. On the plane formed by the two direction vectors of the lines of best fit, the angle them between was on average 2.25 & 1.81 degrees. No significant differences between the algorithm estimates and manually digitized bicipital groove locations was detected. The bicipital groove can be automatically identified with sufficient accuracy to enable its use as a reference feature for the generation of a 3D printed patient specific guide for the humerus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".