ReviewNature reviews. Urology2021
Machine learning for sperm selection.
Review in Nature reviews. Urology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses 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
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.La Radiologia medica · 2025Pooled it
- Artificial intelligence (AI) approaches to male infertility in IVF: a mapping review.European journal of medical research · 2025Pooled it
- Sperm DNA fragmentation and its influence on mammalian reproduction.Nature reviews. Urology · 2026Review
- AI-enabled precision reproductive medicine: a comprehensive review of clinical applications, decision frameworks, and evidence-based implementation.Journal of assisted reproduction and genetics · 2026Review
- Artificial intelligence-driven precision treatment of reproductive medicine-related diseases: the optimal protocol choice for IVF-ET.Journal of advanced research · 2026Article
- Unraveling sperm kinematic heterogeneity with machine learning.Asian journal of andrology · 2026Review
- A Comprehensive Review of Nuclear Mechanics: Advances, Disease Relevance, Methodologies, and AI Applications.Cell biochemistry and biophysics · 2026Review
- Refined trajectory smoothing and deep learning classification of human sperm motility.Human reproduction (Oxford, England) · 2026Article
- An Artificial Intelligence-Based Computer Vision Model for Human Sperm Concentration, Motility, and Kinematics Analysis.Smart medicine · 2026Article
- Sperm Testing in Assisted Reproductive Technology: Diagnosis and Outcome Prediction.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Beyond swimming: emerging parameters for predicting the fertility of mouse spermatozoa.Lab animal · 2025Review
- Automated AI for real-time sperm selection in ICSI: reducing variability and studying the role of sperm in embryo development.Reproductive biology and endocrinology : RB&E · 2025Observational
- Innovations in assisted reproductive technology through the advances in artificial intelligence and image processing.Obstetrics & gynecology science · 2025Review
- [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
- Fresh versus frozen micro-TESE sperm and outcomes.Asian journal of andrology · 2025Review
- Modeling diffusive search by non-adaptive sperm: Empirical and computational insights.PLoS computational biology · 2025Article
- A Narrative Review on the Sperm Selection Methods in Assisted Reproductive Technology: Out with the New, the Old Is Better?Journal of clinical medicine · 2025Review
- Current and Future Applications of Artificial Intelligence to Diagnose and Treat Male Infertility.Advances in experimental medicine and biology · 2025Review
- Assessment of Boar Semen Morphology and Morphometry.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Enhancing Male Fertility Through AI-Based Management of Varicoceles.Current urology reports · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%. Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection. Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection - selecting the most promising candidate from 10
Indexed as
Identifiers
34002070What OpenQuestion holds
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.