Augmented Reality Guided Needle Biopsy of Soft Tissue: A Pilot Study
Bibliographic record
Abstract
Percutaneous biopsies are popular for extracting suspicious tissue formations (primarily for cancer diagnosis purposes) due to the: relatively low cost, minimal invasiveness, quick procedure times, and low risk for the patient. Despite the advantages provided by percutaneous biopsies, poor needle and tumor visualization is a problem that can result in the clinicians classifying the tumor as benign when it was malignant (false negative). The system developed by the authors aims to address the concern of poor needle and tumor visualization through two virtualization setups. This system is designed to track and visualize the needle and tumor in three-dimensional space using an electromagnetic tracking system. User trials were conducted in which the 10 participants, who were not medically trained, performed a total of 6 tests, each guiding the biopsy needle to the desired location. The users guided the biopsy needle to the desired point on an artificial spherical tumor (diameters of 30, 20, and 10 mm) using the 3D augmented reality (AR) overlay for three trials and a projection on a second monitor (TV) for the other three trials. From the randomized trials, it was found that the participants were able to guide the needle tip 6.5 ± 3.3 mm away from the desired position with an angle deviation of 1.96 ± 1.10° in the AR trials, compared to values of 4.5 ± 2.3 mm and 2.70 ± 1.67° in the TV trials. The results indicate that for simple stationary surgical procedures, an AR display is non-inferior a TV display.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".