A novel ultrasound-guided shoulder arthroscopic surgery
Bibliographic record
Abstract
This paper presents a novel ultrasound-guided computer system for arthroscopic surgery of the shoulder joint. Intraoperatively, the system tracks and displays the surgical instruments, such as arthroscope and arthroscopic burrs, relative to the anatomy of the patient. The purpose of this system is to improve the surgeon's perception of the three-dimensional space within the anatomy of the patient in which the instruments are manipulated and to provide guidance towards the targeted anatomy. Pre-operatively, computed tomography images of the patient are acquired to construct virtual threedimensional surface models of the shoulder bone structure. Intra-operatively, live ultrasound images of pre-selected regions of the shoulder are captured using an ultrasound probe whose three-dimensional position is tracked by an optical camera. These images are used to register the surface model to the anatomy of the patient in the operating room. An initial alignment is obtained by matching at least three points manually selected on the model to their corresponding points identified on the ultrasound images. The registration is then improved with an iterative closest point or a sequential least squares estimation technique. In the present study the registration results of these techniques are compared. After the registration, surgical instruments are displayed relative to the surface model of the patient on a graphical screen visible to the surgeon. Results of laboratory experiments on a shoulder phantom indicate acceptable registration results and sufficiently fast overall system performance to be applicable in the operating room.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".