News | From Manual to Intelligent: AI Makes Sperm Selection More Precise
Artificial intelligence (AI) is becoming an increasing focus in assisted reproductive technology. A systematic review recently published in Fertility and Sterility examined the use of AI and machine learning in sperm selection, offering new ways to improve outcomes in assisted reproductive technology (ART).
Why sperm selection matters
More than 100 million people worldwide face infertility, with male factors involved in about 50% of cases. Semen analysis is a key step in diagnosing and treating male infertility and evaluates sperm morphology, motility, and DNA integrity. However, selecting the highest-quality sperm from millions for intracytoplasmic sperm injection (ICSI) is time-consuming and prone to error.
Despite advances in ART, success rates remain limited, partly because sperm selection is not sufficiently precise. Selection currently relies mainly on manual assessment of sperm morphology and motility using World Health Organization (WHO) standards. Time constraints make it difficult for embryologists to evaluate sperm characteristics comprehensively and in detail, which may affect conception outcomes. This makes AI particularly relevant.
AI and machine learning in sperm selection
AI has shown that it can efficiently identify embryos with the greatest developmental and implantation potential while reducing the time and effort embryologists spend on visual assessment and manual grading. By combining genetic and visual data, AI can automate sperm selection and improve precision and efficiency.
AI algorithms can standardize and accelerate sperm morphology analysis. Combined with deep learning, reported accuracy can reach 98%. For example, AI can be trained on high-quality sperm image datasets to assess the morphology, DNA integrity, and motility of individual sperm while reducing subjective human influence.
Key techniques and findings
DNA integrity assessment:
Damage to the sperm head may lead to chromosomal abnormalities, DNA fragmentation, and telomere shortening. Researchers have developed machine-learning algorithms based on the DNA fragmentation index (DFI), using methods such as single-cell gel electrophoresis (SCGE) and sperm chromatin structure assays to assess individual sperm quality accurately.
Motility analysis:
AI combines computer-assisted sperm analysis (CASA), microfluidic chips, and holographic imaging to improve the analysis of sperm movement. For example, mathematical models based on three-dimensional (3D) helical tail motion can predict swimming ability more precisely.
Multiparameter optimization:
AI systems integrate sperm morphology, movement patterns, DNA integrity, and other data to select the best sperm automatically, significantly improving conception and pregnancy rates in ART.
Future outlook
As AI algorithms improve, assisted reproduction laboratories can use larger, higher-quality sperm image datasets to increase selection efficiency further. The technology not only reduces embryologists' workload but also offers greater hope to people facing infertility.
News | From Manual to Intelligent: AI Makes Sperm Selection More Precise
News | From Manual to Intelligent: AI Makes Sperm Selection More Precise
Artificial intelligence (AI) is becoming an increasing focus in assisted reproductive technology. A systematic review recently published in Fertility and Sterility examined the use of AI and machine learning in sperm selection, offering new ways to improve outcomes in assisted reproductive technology (ART).
Why sperm selection matters
More than 100 million people worldwide face infertility, with male factors involved in about 50% of cases. Semen analysis is a key step in diagnosing and treating male infertility and evaluates sperm morphology, motility, and DNA integrity. However, selecting the highest-quality sperm from millions for intracytoplasmic sperm injection (ICSI) is time-consuming and prone to error.
Despite advances in ART, success rates remain limited, partly because sperm selection is not sufficiently precise. Selection currently relies mainly on manual assessment of sperm morphology and motility using World Health Organization (WHO) standards. Time constraints make it difficult for embryologists to evaluate sperm characteristics comprehensively and in detail, which may affect conception outcomes. This makes AI particularly relevant.
AI and machine learning in sperm selection
AI has shown that it can efficiently identify embryos with the greatest developmental and implantation potential while reducing the time and effort embryologists spend on visual assessment and manual grading. By combining genetic and visual data, AI can automate sperm selection and improve precision and efficiency.
AI algorithms can standardize and accelerate sperm morphology analysis. Combined with deep learning, reported accuracy can reach 98%. For example, AI can be trained on high-quality sperm image datasets to assess the morphology, DNA integrity, and motility of individual sperm while reducing subjective human influence.
Key techniques and findings
DNA integrity assessment:
Damage to the sperm head may lead to chromosomal abnormalities, DNA fragmentation, and telomere shortening. Researchers have developed machine-learning algorithms based on the DNA fragmentation index (DFI), using methods such as single-cell gel electrophoresis (SCGE) and sperm chromatin structure assays to assess individual sperm quality accurately.
Motility analysis:
AI combines computer-assisted sperm analysis (CASA), microfluidic chips, and holographic imaging to improve the analysis of sperm movement. For example, mathematical models based on three-dimensional (3D) helical tail motion can predict swimming ability more precisely.
Multiparameter optimization:
AI systems integrate sperm morphology, movement patterns, DNA integrity, and other data to select the best sperm automatically, significantly improving conception and pregnancy rates in ART.
Future outlook
As AI algorithms improve, assisted reproduction laboratories can use larger, higher-quality sperm image datasets to increase selection efficiency further. The technology not only reduces embryologists' workload but also offers greater hope to people facing infertility.
Source:
Collected online