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

In: 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
Limited metadata. Only one source feed has indexed this record so far — no abstract, full text, or open-access copy is available through Endo Lab. The publisher's page (linked below) is the canonical location for the actual content. If you have institutional access, use "Find at my library".
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

My notes (saved in your browser only)

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: 2026-06-14T06:08:20.186862+00:00
openalex
last seen: 2026-06-14T06:02:12.833719+00:00
unpaywall
last seen: 2026-06-14T06:15:46.576397+00:00
License: CC0 · commercial use OK