AIMC Topic: Sperm Retrieval
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FertilitY Predictor-a machine learning-based web tool for the prediction of assisted reproduction outcomes in men with Y chromosome microdeletions.
Journal of assisted reproduction and genetics
Dec 9, 2024
PURPOSE: Y chromosome microdeletions (YCMD) are a common cause of azoospermia and oligozoospermia in men. Herein, we developed a machine learning-based web tool to predict sperm retrieval rates and success rates of assisted reproduction (ART) in men ...
Prediction of testicular histology in azoospermia patients through deep learning-enabled two-dimensional grayscale ultrasound.
Asian journal of andrology
Oct 4, 2024
Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) ...
Artificial intelligence interpretation of touch print smear cytology of testicular specimen from patients with azoospermia.
Journal of assisted reproduction and genetics
Sep 3, 2024
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)
Aug 27, 2024
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...
Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study.
BMC urology
Jul 29, 2024
BACKGROUND: The relationship between surgical sperm retrieval of different etiologies and clinical pregnancy is unclear. We aimed to develop a robust and interpretable machine learning (ML) model for predicting clinical pregnancy using the SHapley Ad...
Evaluation of an artificial intelligence-facilitated sperm detection tool in azoospermic samples for use in ICSI.
Reproductive biomedicine online
Feb 22, 2024
RESEARCH QUESTION: Can artificial intelligence (AI) improve the efficiency and efficacy of sperm searches in azoospermic samples?
Clinical parameters as predictors for sperm retrieval success in azoospermia: experience from Indonesia.
F1000Research
Dec 6, 2023
BACKGROUND: Azoospermia is the most severe type of male infertility. This study aimed to identify useful clinical parameters to predict sperm retrieval success. This could assist clinicians in accurately diagnosing and treating patients based on the ...
Automated rare sperm identification from low-magnification microscopy images of dissociated microsurgical testicular sperm extraction samples using deep learning.
Fertility and sterility
May 10, 2022
OBJECTIVE: To develop a machine learning algorithm to detect rare human sperm in semen and microsurgical testicular sperm extraction (microTESE) samples using bright-field (BF) microscopy for nonobstructive azoospermia patients.
Machine learning for sperm selection.
Nature reviews. Urology
May 17, 2021
Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has rem...
Effects of hormonal stimulation on the concentration and quality of excreted spermatozoa in the critically endangered Panamanian golden frog (Atelopus zeteki).
Theriogenology
Dec 27, 2016
Knowledge of basic gamete biology is critical to better protect and propagate endangered amphibian species and also to develop reproductive technologies combined with germplasm cryopreservation. The objectives of the study were to test different horm...
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