AIMC Topic: Testis

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Machine learning models in predicting viability after testicular torsion: a proof of concept study.

Pediatric surgery international
PURPOSE: Decision-making for orchiectomy following testicular torsion often relies on subjective clinical evaluations. This study investigates the efficacy of machine learning (ML) models in objectively predicting post-torsion testicular viability, a...

CPN2 alleviates cryptorchidism by inhibiting the NF-κB pathway and regulating immune responses.

Autoimmunity
Cryptorchidism, a common male reproductive disorder characterized by undescended testes, is associated with infertility and increased cancer risk. While its etiology remains incompletely understood, accumulating evidence suggests that immune-inflamma...

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.

Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model.

Andrology
BACKGROUND: Histological analysis of the testicular sections is paramount in infertility research but tedious and often requires months of training and practice.

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) ...

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...

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...

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...

The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis.

Andrology
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...