Image-aware mixture of experts automates the detection of ovarian follicle rupture in a high-throughput ex vivo ovulation assay

Clicks: 23
ID: 327415
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #81 of 188 articles by views in biology of reproduction

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 188 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
An AI-based dynamic, image-aware Mixture-of-Experts (MoE) framework automates ex vivo ovulation detection and offers a highly accurate, scalable solution for studying the biology of ovulation, drug development, and reproductive toxicity testing.
Reference Key
openalex_W7207515854 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal biology of reproduction
Year 2026
DOI
10.1093/biolre/ioag189
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.