Two‐Step Parametrial Endometriosis Nodule Excision Using Virtual Reality Technology and 3D Modelling for Surgical Planning
Bibliographic record
Abstract
Extensive and infiltrative fibrous adhesions of the uterus and ovaries to the surrounding organs make surgical interventions in endometriosis challenging. A preoperative identification of these involvements can help the surgeon better prepare for the surgery. Traditionally, ultrasonography and magnetic resonance imaging (MRI) have been used. However, clinical use of modern VR technology for creating and visualising a three-dimensional (3D) digital model for a complex surgical case has been proposed. We describe a case of a 29-year-old who presented with dyspareunia and dysmenorrhea (VAS score of 10/10) with left parametrial endometriosis and created a 3D model from their two-dimensional (2D) DICOM images. A left parametrial endometriosis nodule was identified involving the left ureter, rectum, and vaginal fornix along with mucosa. A virtual preoperative surgery was done for precise and complete excision of the disease and to prevent injury to the left ureter and rectum. The surgery was performed as a two-step excision using a da Vinci Xi robot and included left ureterolysis, shaving of the bowel endometriosis nodule and full-thickness vaginal wall excision along with the infiltrating nodule. The infiltrating endometriosis nodule was split into two halves and was excised individually. Her postoperative VAS score for dysmenorrhea was 2/10, and she is 28 weeks pregnant at the time of submission. Advanced VR imaging can help in the evaluation and management of deep endometriosis. It can improve the surgeon's understanding of the specific anatomy, visualise the disease, and improve clinical outcomes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".