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[A CASE OF TESTICULAR TUMOR UNDER CONSIDERATION FOR PARTIAL ORCHIECTOMY THROUGH RAPID INTRAOPERATIVE DIAGNOSIS].

Nihon Hinyokika Gakkai zasshi. The japanese journal of urology
A 35-year-old man visited a local doctor for continuing analysis of his infertility. Semen analysis revealed azoospermia while an ultrasonography detected a right testicular tumor with a diameter of 10 mm. A blood test was negative for tumor markers....

Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning.

Journal of assisted reproduction and genetics
PURPOSE: Rapid and easy detection of spermatogonial stem/progenitor cells (SSPCs) is crucial for clinicians dealing with male infertility caused by prepubertal testicular damage. Deep learning (DL) methods may offer visual tools for tracking SSPCs on...

An automatic classification method of testicular histopathology based on SC-YOLO framework.

BioTechniques
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...

Deep Learning-Based Spermatogenic Staging in Tissue Sections of Cynomolgus Macaque Testes.

Toxicologic pathology
The indirect assessment of adverse effects on fertility in cynomolgus monkeys requires that tissue sections of the testis be microscopically evaluated with awareness of the stage of spermatogenesis that a particular cross-section of a seminiferous tu...

[Artificial intelligence: to a better predictive strategy for testicular sperm extraction outcome in azoospermia].

Annales de biologie clinique
Azoospermia, defined as the absence of sperm in the semen, is found in 10-15 % of infertile patients. Two-thirds of these cases are caused by impaired spermatogenesis, known as non-obstructive azoospermia (NOA). In this context, surgical sperm extrac...

Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study.

BMC urology
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...

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...

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...

Multiomics identification of programmed cell death-related characteristics for nonobstructive azoospermia based on a 675-combination machine learning computational framework.

Genomics
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
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) ...