NS-AUA 2024 Annual Meeting Abstracts – Education, Laparoscopy, Robotics, Surgical Innovation
Bibliographic record
Abstract
Introduction: Transrectal ultrasound guided (TRUS) prostate biopsies are the gold standard for diagnosis of prostate cancer, the most common cancer among men in the United States; however, discomfort associated with the transrectal probe can lead to patient reluctance to follow through with prostate biopsies.Augmented reality (AR) has emerged as a paradigm-shifting technology in surgery by superimposing digital information (i.e., 3D volumetric renderings of patient scans) on top of the physical world (i.e., the respective patient's body).In this proof of concept, we explore the feasibility of an AR-guided non-TRUS transperineal prostate biopsy.Methods: Computerized tomography (CT) imaging of two supine male cadaveric torsos was uploaded to the SurgicalAR system (Medivis, New York, NY) and a 3D volumetric hologram was projected through the see-through visor of the HoloLens 2 (Microsoft, Seattle, WA).The hologram was registered to the cadaver using a point-to-point framework relying on pre-identified cadaveric landmarks matched to virtual counterparts.While wearing the HoloLens and using SurgicalAR, virtual trajectories towards the pre-selected prostate targets were planned by first selecting a prostate target and subsequently selecting a superficial entry point.The trajectories were then projected through the visor.A needle was inserted following the virtual plans and 1cc of radiopaque dye was injected.The cadavers were rescanned to highlight dye tract and target.Results: Three trajectories were planned using the AR system.In two of the trajectories, the target and entry points were both planned with the senior author positioned caudally; in the other trajectory, the senior author planned the target while positioned rostrally and the entry point while positioned caudally.Following the latter trajectory, the prostate target was hit successfully without damage to local structures.Conclusions: To the best of our knowledge, we present the first cadaveric study of non-TRUS guided transperineal prostate biopsy using AR guidance.This feasibility study paves the way for future studies and clinical trials using AR in place of TRUS to guide transperineal prostate biopsy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.342 | 0.202 |
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".