AIMC Topic: Semen

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Identifying predictors of Day 5 blastocyst utilization rate using an artificial neural network.

Reproductive biomedicine online
RESEARCH QUESTION: Can artificial intelligence identify predictors of an increased Day 5 blastocyst utilization rate (D5BUR), which is one of the most informative key performance indicators in an IVF laboratory?

Deep learning-based method for analyzing the optically trapped sperm rotation.

Scientific reports
Optical tweezers exert a strong trapping force on cells, making it crucial to analyze the movement of trapped cells. The rotation of cells plays a significant role in their swimming patterns, such as in sperm cells. We proposed a fast deep-learning-b...

A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm.

Sensors (Basel, Switzerland)
Infertility has become a common problem in global health, and unsurprisingly, many couples need medical assistance to achieve reproduction. Many human behaviors can lead to infertility, which is none other than unhealthy sperm. The important thing is...

Improving outcomes of assisted reproductive technologies using artificial intelligence for sperm selection.

Fertility and sterility
Within the field of assisted reproductive technology, artificial intelligence has become an attractive tool for potentially improving success rates. Recently, artificial intelligence-based tools for sperm evaluation and selection during intracytoplas...

Structures of sperm flagellar doublet microtubules expand the genetic spectrum of male infertility.

Cell
Sperm motility is crucial for successful fertilization. Highly decorated doublet microtubules (DMTs) form the sperm tail skeleton, which propels the movement of spermatozoa. Using cryo-electron microscopy (cryo-EM) and artificial intelligence (AI)-ba...

Artificial intelligence for sperm selection-a systematic review.

Fertility and sterility
Despite the increasing number of assisted reproductive technologies based treatments being performed worldwide, there has been little improvement in fertilization and pregnancy outcomes. Male infertility is a major contributing factor, and sperm eval...

Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade.

Fertility and sterility
The integration of artificial intelligence (AI) and deep learning algorithms into medical care has been the focus of development over the last decade, particularly in the field of assisted reproductive technologies and in vitro fertilization (IVF). W...

New horizons in human sperm selection for assisted reproduction.

Frontiers in endocrinology
Male infertility is a commonly encountered pathology that is estimated to be a contributory factor in approximately 50% of couples seeking recourse to assisted reproductive technologies. Upon clinical presentation, such males are commonly subjected t...

New frontiers in embryo selection.

Journal of assisted reproduction and genetics
Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gai...

Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks.

Journal of assisted reproduction and genetics
PURPOSE: To determine if deep learning artificial intelligence algorithms can be used to accurately identify key morphologic landmarks on oocytes and cleavage stage embryo images for micromanipulation procedures such as intracytoplasmic sperm injecti...