Optimizing endometriosis diagnosis and mapping: The important role of advanced transvaginal ultrasound
Bibliographic record
Abstract
Endometriosis is estimated to affect 5%-10% of women of reproductive age, making timely diagnosis essential for initiating treatment, alleviating symptoms, and reducing the risk of disease progression. Unfortunately, the diagnostic delay in this disease is estimated to be approximately 10 years. The aim of this study is to present a case series of three patients assessed with both imaging modalities for endometriosis diagnosis and mapping, advanced transvaginal ultrasound (ATVUS) and magnetic resonance imaging (MRI). The findings obtained by performing the ATVUS imaging study protocol with different pelvic compartments according to the International Deep Endometriosis Analysis (IDEA) consensus are described and contrasted with those for MRI, along with their correlation to surgical and histological findings. A single gynecologist with specialized training in ATVUS performed a systematic pelvic evaluation in patients with clinical suspicion of endometriosis. The physician performed the anatomo-sonographic assessments described by the IDEA consensus. A retrospective analysis of the three cases was performed comparing both imaging modalities and surgical and histological findings. It was demonstrated in this case series that endometriosis is detectable in distinct pelvic compartments by ATVUS, with results comparable to MRI, while offering the benefits of lower cost and widespread accessibility. In contrast, individuals, insurance companies, and healthcare systems in some countries might be unable or unwilling to cover MRI costs for endometriosis diagnosis or presurgical mapping. In conclusion, given the high prevalence of endometriosis, mastering ATVUS is essential. Future studies should aim to robustly evaluate the role of ATVUS alongside other imaging modalities, including MRI, to maximize diagnostic accuracy.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".