AIMC Topic: Spermatozoa

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Modeling and control of a sperm-inspired robot with helical propulsion.

Bioinspiration & biomimetics
Efficient propulsion has been a central focus of research in the field of biomimetic underwater vehicles. Compared to the prevalent fish-like reciprocating flapping propulsion mode, the sperm-like helical propulsion mode features higher efficiency an...

Artificial intelligence in Andrological flow cytometry: The next step?

Animal reproduction science
Since its introduction in animal andrology, flow cytometry (FC) has dramatically evolved. Nowadays, many compartments and functions of the spermatozoa can be analyzed in thousands of spermatozoa, including, but not limited to DNA, acrosome, membrane ...

Deep learning classification method for boar sperm morphology analysis.

Andrology
BACKGROUND: Boar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subj...

Artificial intelligence interpretation of touch print smear cytology of testicular specimen from patients with azoospermia.

Journal of assisted reproduction and genetics
PURPOSE: Identification of mature sperm at microdissection testicular sperm extraction (mTESE) is a crucial step of sperm retrieval to help patients with non-obstructive azoospermia (NOA) proceed to intracytoplasmic sperm injection. Touch print smear...

Development of a deep-learning model for detecting positive tubules during sperm recovery for nonobstructive azoospermia.

Reproduction (Cambridge, England)
To enhance surgical testicular sperm retrieval outcome for men with nonobstructive azoospermia, a deep-learning model was developed to identify positive seminiferous tubules by labeling 110 images with sperm-containing tubules sampled during microdis...

Multimodal distribution and its impact on the accurate assessment of spermatozoa morphological data: Lessons from machine learning.

Animal reproduction science
Objective assessment of sperm morphology is an essential component for assessing ejaculate quality. Due to economic limitations, investigators often divert to conducting observational studies instead of experimental ones, which provide the strongest ...

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 (...