AIMC Topic: Spermatozoa

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Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study.

Reproductive biology and endocrinology : RB&E
BACKGROUND: Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment o...

Only the Best of the Bunch-Sperm Preparation Is Not Just about Numbers.

Seminars in reproductive medicine
In this , we present an overview of the current and emerging methods and technologies for optimizing the man and the sperm sample for fertility treatment. We argue that sperms are the secret to success, and that there are many avenues for improving b...

Application of artificial intelligence in gametes and embryos selection.

Human fertility (Cambridge, England)
Gamete and embryo quality are critical to the success rate of Assisted Reproductive Technology (ART) cycles, but there remains a lack of methods to accurately measure the quality of sperm, oocytes and embryos. The ability of Artificial Intelligence (...

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...

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...