From Imaging to Visualization: Seeing the Future of Endometriosis Care
Bibliographic record
Abstract
STUDY OBJECTIVE: To describe how the knowledge from standard imaging practices can be translated into 3-dimensional visualization techniques and used in the surgical planning and management of endometriosis. DESIGN: Two case studies of patients with endometriosis are described. SETTING: Tertiary care academic center. INTERVENTIONS: Transvaginal ultrasound [1], magnetic resonance imaging, 3-dimensional printing [2], and 3-dimensional virtual reality modeling [3] were used during patient workup and preparation. Three-dimensional modeling was performed by a virtual reality technician and verified for accuracy by a fellowship-trained radiologist. Surgical management for endometriosis was performed. CONCLUSION: Although expert transvaginal ultrasound and magnetic resonance imaging suffice for most cases, 3-dimensional printing and virtual reality modeling are a novel adjunct to standard imaging modalities. Rendering 2-dimensional images into a 3-dimensional representation allows users to interact with the anatomy and is particularly useful when distorted by complex pathology. These techniques contributed to improved patient understanding and experience and helped medical learners better grasp regular imaging techniques and its translation to pelvic anatomy. Finally, it augmented surgeon comprehension of the relationship between the pelvic structures, allowing for enhanced surgical planning and intraoperative decision making. Further study is being performed to quantify these effects.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".