AIMC Topic: Testis
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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...
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...
The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis.
Andrology
Apr 5, 2024
BACKGROUND: Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines...
Deep Learning-Based Spermatogenic Staging in Tissue Sections of Cynomolgus Macaque Testes.
Toxicologic pathology
Mar 11, 2024
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...
Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning.
Journal of assisted reproduction and genetics
Mar 30, 2023
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...
Morphologic Features and Deep Learning-Based Analysis of Canine Spermatogenic Stages.
Toxicologic pathology
Aug 24, 2022
In nonclinical toxicity studies, stage-aware evaluation is often expected to assess drug-induced testicular toxicity. Although stage-aware evaluation does not require identification of specific stages, it is important to understand microscopic featur...
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