ArticleBiology of reproduction2017
CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns.
Article in Biology of reproduction, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 88 citations in OpenAlex.
- Use of Artificial Intelligence in the choice of sperm for ICSI - A Literature Review.JBRA assisted reproduction · 2026Pooled it
- Artificial intelligence (AI) approaches to male infertility in IVF: a mapping review.European journal of medical research · 2025Pooled it
- Refined trajectory smoothing and deep learning classification of human sperm motility.Human reproduction (Oxford, England) · 2026Article
- Unraveling sperm kinematic heterogeneity with machine learning.Asian journal of andrology · 2026Review
- 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
- Modeling diffusive search by non-adaptive sperm: Empirical and computational insights.PLoS computational biology · 2025Article
- [Advances in the Application of Artificial Intelligence in the Field of Male Infertility].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025Review
- Artificial Intelligence in Andrology: A New Frontier in Male Infertility Diagnosis and Treatment.Current urology reports · 2025Review
- The future of embryo engineering and fertility research in interdisciplinary collaboration.Frontiers in cell and developmental biology · 2025Review
- [Application of Artificial Intelligence in Sperm Quality Analysis and Sperm Screening].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Review
- Article
- Microfluidic chip as a promising evaluation method in assisted reproduction: A systematic review.Bioengineering & translational medicine · 2024Review
- Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis.Medicina (Kaunas, Lithuania) · 2024Review
- Pyruvate modulation of redox potential controls mouse sperm motility.Developmental cell · 2024Article
- Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics.The world journal of men's health · 2024Review
- Sperm motility assessed by deep convolutional neural networks into WHO categories.Scientific reports · 2023Article
- Article
- Automation in ART: Paving the Way for the Future of Infertility Treatment.Reproductive sciences (Thousand Oaks, Calif.) · 2023Review
- Artificial Intelligence in Reproductive Medicine - An Ethical Perspective.Geburtshilfe und Frauenheilkunde · 2023Article
- Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
The ability to accurately monitor alterations in sperm motility is paramount to understanding multiple genetic and biochemical perturbations impacting normal fertilization. Computer-aided sperm analysis (CASA) of human sperm typically reports motile percentage and kinematic parameters at the population level, and uses kinematic gating methods to identify subpopulations such as progressive or hyperactivated sperm. The goal of this study was to develop an automated method that classifies all patterns of human sperm motility during in vitro capacitation following the removal of seminal plasma. We visually classified CASA tracks of 2817 sperm from 18 individuals and used a support vector machine-based decision tree to compute four hyperplanes that separate five classes based on their kinematic parameters. We then developed a web-based program, CASAnova, which applies these equations sequentially to assign a single classification to each motile sperm. Vigorous sperm are classified as progressive, intermediate, or hyperactivated, and nonvigorous sperm as slow or weakly motile. This program correctly classifies sperm motility into one of five classes with an overall accuracy of 89.9%. Application of CASAnova to capacitating sperm populations showed a shift from predominantly linear patterns of motility at initial time points to more vigorous patterns, including hyperactivated motility, as capacitation proceeds. Both intermediate and hyperactivated motility patterns were largely eliminated when sperm were incubated in noncapacitating medium, demonstrating the sensitivity of this method. The five CASAnova classifications are distinctive and reflect kinetic parameters of washed human sperm, providing an accurate, quantitative, and high-throughput method for monitoring alterations in motility.
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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.