Advancing Knee Arthroscopy Surgeries with Endoscopic and B-Mode Ultrasound Imaging
Bibliographic record
Abstract
Knee arthroscopy is a minimally invasive surgery where surgeons utilize arthroscopes to visualize the internal structure of the knee to evade the need of large incisions. However, conventional knee arthroscopy currently lacks long term effectiveness which can be partially attributed to the lack of visibility: current arthroscopes provide only superficial morphology of tissues and hence leads to inaccuracy procedures. To solve this issue, combining ultrasound imaging in conjunction with the traditional optical modality has been investigated. An external ultrasound can allow the surgeon to track the whereabouts of the surgical instruments inside the knee, while an ultrasound arthroscope using a modified intravenous ultrasound can allow the surgeon to receive depth-resolved information, such as evaluating the properties and integrity of cartilage and tissue around the joint in three-dimension (3D) structure. In this work, we developed a simulation program to mimic ultrasound arthroscopy implementations for optimizing the design of ultrasound arthroscopy devices for knee surgery. Moreover, a 3D model of the human knee was developed and cross-sectional images from this model served as the imaging targets for the simulation. Ultrasound images with high consistency to targets were received in the simulation. The optimized combination of using endoscopic (internal) and B-mode (external) ultrasound imaging is a new design that can create positive outcomes for many different stakeholders. Patients will benefit from an improved surgery procedure which ensures less error and a better outcome. It is anticipated that this study will serve as a stepping stone for future research and eventually test trials that employ the dual ultrasound devices method in clinical settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".