Review of Endometriosis Diagnosis through Advances in Biomedical Engineering
Bibliographic record
Abstract
Endometriosis is characterized as the ectopic presence of endometrium in various locations within the abdominal cavity, such as the fallopian tube, the pouch of Douglas, the ovaries, outside the ovaries, and more. The inner lining of the uterus, endometrium, is a dynamic tissue that undergoes morphological and functional changes cyclically. The proliferation of endometrial cells during menstruation is influenced by increasing circulating estrogen levels. Adult progenitor stem cells are likely responsible for this remarkable regenerative capacity and hence, enhanced capacity to generate endometriosis. This chronic estrogen-dependent disease is characterized by the ectopic endometrial implant. The disorder occurs in 6%-10% of reproductive-aged women and in 35%-50% of women with pelvic pain and infertility. Currently, the preferred diagnostic methods are laparoscopic inspection by transvaginal ultrasound, MRI, and sensors. Diagnoses with transvaginal ultrasound have 92.7% sensitivity and 97% specificity in detecting endometriotic lesions. On average, MRI diagnoses of DIE have 74% sensitivity and 95% specificity. Lastly, chemical sensors have 91.7% sensitivity and 90.0% specificity in detecting endometriosis. The standard of care includes personalizing the treatment plan based on the individual's set of symptoms and their severity. Advances in biomedical engineering have aided professionals in personalizing the course of treatments as well as to increasing the quality of life of these patients through various therapies for managing pain. Because no one theory provides a full explanation for the manifestation of the disease, hormonal therapies, targeted therapeutics, and surgical options have emerged as elements of disease management. Clinicians are in the process of developing advanced pharmaceutical drugs with specific orphan target receptors of the ectopic tissue. Possible complications that accompany the condition include dyspareunia, hyperalgesia, infertility, and many others.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".