AIMC Topic: Azoospermia
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Developing a nomogram model for predicting non-obstructive azoospermia using machine learning techniques.
Scientific reports
Feb 14, 2025
Azoospermia, defined by the absence of sperm in the ejaculate, manifests as obstructive azoospermia (OA) or non-obstructive azoospermia (NOA). Reliable predictive models utilizing biomarkers could aid in clinical decision-making. This study included ...
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 ...
Multiomics identification of programmed cell death-related characteristics for nonobstructive azoospermia based on a 675-combination machine learning computational framework.
Genomics
Dec 9, 2024
BACKGROUND: Abnormal programmed cell death (PCD) plays a central role in spermatogenic dysfunction. However, the molecular mechanisms and biomarkers of PCD in patients with nonobstructive azoospermia (NOA) remain unclear.
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) ...
An automatic classification method of testicular histopathology based on SC-YOLO framework.
BioTechniques
Sep 12, 2024
The pathological diagnosis and treatment of azoospermia depend on precise identification of spermatogenic cells. Traditional methods are time-consuming and highly subjective due to complexity of Johnsen score, posing challenges for accurately diagnos...
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...
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.
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