ArticleNature communications2025
Explainable artificial intelligence to identify follicles that optimize clinical outcomes during assisted conception.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- Problems in the use of chemical and functional nomenclatures for steroids in human physiology, biology and pharmacology.RSC advances · 2026Review
- Utilization of artificial intelligence in Men's Health: Opportunities for innovation and quality improvement.International journal of impotence research · 2026Review
- Explainable Artificial Intelligence in Assisted Reproductive Technology: Bridging Prediction and Clinical Judgment.Biomedicines · 2026Review
- The potential, perils and pitfalls of Artificial intelligence (AI) in Assisted Reproductive Technologies (ART).Reproductive biology and endocrinology : RB&E · 2026Review
- Epitranscriptomic modifications in embryonic development: insights into natural and ART-induced mechanisms and implications.Human reproduction update · 2026Review
- Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Harmonization and standardization for selection of high-trueness estradiol assay kits in serum and evaluation of estradiol levels relative to follicle diameters on trigger days.Frontiers in medicine · 2026Article
- Medium-sized follicle proportion on the trigger day may be associated with higher live birth rate in fresh embryo transfer cycles among low-prognosis patients: a retrospective cohort study.Frontiers in endocrinology · 2026Article
- Late follicular phase progesterone levels andFrontiers in endocrinology · 2026Article
- Interpretable machine learning model for predicting MII oocyte retrieved following controlled ovarian stimulation: a retrospective cohort study of 24,976 IVF/ICSI cycles.Journal of assisted reproduction and genetics · 2025Article
- [Progress in the application of artificial intelligence in assisted reproduction].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Review
- On the perils of prematurity-deconvolving luteinization from physiology to cycle management.Journal of assisted reproduction and genetics · 2025Article
- Pericytes promote metastasis by regulating tumor local vascular tone and hemodynamics.Nature communications · 2025Article
- Seeing less with more: extending the Goldilocks hypothesis across the ART journey.Journal of assisted reproduction and genetics · 2025Article
- Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study.Scientific reports · 2025Article
- The quality of human eggs and its pre-IVF incubation.Reproductive medicine and biologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Infertility affects one-in-six couples, often necessitating in vitro fertilization treatment (IVF). IVF generates complex data, which can challenge the utilization of the full richness of data during decision-making, leading to reliance on simple 'rules-of-thumb'. Machine learning techniques are well-suited to analyzing complex data to provide data-driven recommendations to improve decision-making. In this multi-center study (n = 19,082 treatment-naive female patients), including 11 European IVF centers, we harnessed explainable artificial intelligence to identify follicle sizes that contribute most to relevant downstream clinical outcomes. We found that intermediately-sized follicles were most important to the number of mature oocytes subsequently retrieved. Maximizing this proportion of follicles by the end of ovarian stimulation was associated with improved live birth rates. Our data suggests that larger mean follicle sizes, especially those >18 mm, were associated with premature progesterone elevation by the end of ovarian stimulation and a negative impact on live birth rates with fresh embryo transfer. These data highlight the potential of computer technologies to aid in the personalization of IVF to optimize clinical outcomes pending future prospective validation.
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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 Socratic graph.