ArticleJournal of assisted reproduction and genetics2026
An artificial intelligence platform for automated measurement and count estimation of ovarian follicles during ovarian stimulation and IVF: a multicenter study.
Article in Journal of assisted reproduction and genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in ovarian pathophysiology and management: a systematic review and meta-analysis.Journal of ovarian research · 2026Pooled it
- Implementation readiness of artificial intelligence inFrontiers in endocrinology · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeUltrasound measurement of follicle diameter is essential in IVF monitoring. This study evaluates the analytical performance of follicle counts and size measurements from two-dimensional images using an AI-based platform, compared to assessments by certified sonographers.
methodsA total of 5508 TVUS scans from 1689 patients undergoing controlled ovarian stimulation across four IVF centers (Poland, Argentina, Colombia, and the USA) were retrospectively analyzed. All visible follicles were marked using bounding boxes. The dataset included three subsets: training/validation for model development, independent test for evaluating performance across ultrasound systems, and a consensus test set (102 scans from 27 patients) annotated by three expert sonographers. Model performance was assessed using precision, recall, and F1 score. Annotation efficiency was measured by comparing manual and AI-assisted times. Real-world performance was evaluated on a prospective cohort of 904 scans from 269 patients, based on expert adjustments to AI annotations.
resultsFor follicles ≥ 10 mm, the model achieved 98.2% precision (95% CI, 96.5-99.2), 88.9% recall (85.0-91.8), and 93.3% F1 score (90.7-95.1). For all follicles, precision and recall were 94.2% (92.8-95.4) and 68.9% (65.9-71.9). Annotation time was reduced 2.5-fold (p < 0.01), with an average of 0.54 expert adjustments per scan (CI, 0.47-0.62). Model performance was stable across ultrasound platforms.
conclusionThis AI platform enables accurate, automated follicle counting and measurement during ovarian stimulation. It matches expert-level performance, improves efficiency, and supports scalable, cost-effective fertility care without compromising quality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.