Machine learning models for non-invasive endometriosis triage using a laparoscopically and histologically verified cohort

Marina Paula Andres, Andres MP, Rufino CDC, Clarisse da Costa Rufino, Ramanathan Anantharama Krishnan, Krishnan RA, Sidharth Jain, Jain S, Bruno Duarte Gomes, Mauricio S Abrão, Mauricio S Abrao, Abrao MS
European journal of obstetrics, gynecology, and reproductive biology · 2026 · vol. 324 , pp. 115237 · doi:10.1016/j.ejogrb.2026.115237 · PMID:42275943 · W7163816619
article OA: closed CC0
View on OpenAlex View on PubMed View at publisher
⚙ AI-generated summary by claude@2026-06, 2026-06-13 ⓘ

This study developed machine learning models to triage patients for endometriosis using data from a cohort verified by laparoscopy and histology.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

Abstract

OBJECTIVE: To develop and internally validate machine-learning models for non-invasive triage of women at risk for endometriosis using structured clinical variables in a laparoscopically and histologically verified cohort. STUDY DESIGN: This retrospective study included 2546 women who underwent laparoscopic surgery between 2008 and 2023 at two tertiary referral centers in São Paulo, Brazil. Endometriosis was confirmed in 1983 patients and absent in 563 controls, corresponding to an enriched tertiary-care case prevalence of 77.9%. Two feature-selection strategies were compared: clinician-guided selection and statistically optimized selection. Preprocessing, feature selection, MinMax scaling, and Synthetic Minority Oversampling Technique (SMOTE) were performed within the training workflow of stratified 10-fold cross-validation to minimize information leakage. Primary performance measures were F1-score, recall, positive predictive value (PPV), negative predictive value (NPV), and AUC-ROC; accuracy was reported only as a secondary metric. RESULTS: Statistically optimized feature selection produced modest but consistent improvements in discrimination and F1-score across most models. XGBoost achieved the highest F1-score in the statistically optimized analysis (0.916, 95% CI 0.909-0.922), with recall of 0.932 (95% CI 0.921-0.943), PPV of 0.900 (95% CI 0.894-0.906), NPV of 0.730 (95% CI 0.700-0.761), and AUC-ROC of 0.895 (95% CI 0.887-0.904). The ensemble model achieved the highest PPV (0.924, 95% CI 0.899-0.947). The most informative variables included infertility, dysmenorrhea, pain level, cyclic intestinal pain, abnormal vaginal examination, number of diseases reported, and menstrual-flow characteristics. CONCLUSION: Machine-learning models based on structured clinical variables may support non-invasive triage of women at risk for endometriosis. The contribution of the revised framework is the use of a clinically verified cohort, transparent feature selection, and leakage-aware internal validation rather than removal of diagnostically challenging records. Because this retrospective study was internally validated in tertiary referral centers with enriched disease prevalence, external validation, local calibration, and prospective clinical utility assessment are required before implementation.

My notes (saved in your browser only)

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (31)

Source provenance

europepmc
last seen: 2026-08-11T06:11:44.160905+00:00
openalex
last seen: 2026-08-11T06:05:05.117159+00:00
pubmed
last seen: 2026-08-11T06:06:59.164160+00:00
unpaywall
last seen: 2026-08-10T06:43:36.850308+00:00
License: CC0 · commercial use OK