AIMC Topic: Semen Analysis

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Use of a sperm morphology assessment standardisation training tool improves the accuracy of novice sperm morphologists.

Scientific reports
Sperm morphology assessment is recognised as a critical, yet variable, test of male fertility. This variability is due in part to the lack of standardised training for morphologists. This study utilised a bespoke 'Sperm Morphology Assessment Standard...

Semen collection, semen analysis and artificial insemination in the kākāpō (Strigops habroptilus) to support its conservation.

PloS one
The critically endangered kākāpō (Strigops habroptilus) has suffered population declines due to habitat loss, hunting, and predation. Conservation efforts, including translocation to predator-free islands, have helped increase numbers of this flightl...

Artificial Intelligence in Andrology: A New Frontier in Male Infertility Diagnosis and Treatment.

Current urology reports
PURPOSE OF REVIEW: Infertility affects approximately 15% of couples globally, with male-factor infertility contributing to about half of these cases. Despite advancements in reproductive medicine, particularly in surgical methods, the prevalence of m...

Developing a nomogram model for predicting non-obstructive azoospermia using machine learning techniques.

Scientific reports
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 ...

Enhancing Male Fertility Through AI-Based Management of Varicoceles.

Current urology reports
REVIEW PURPOSE: The clinical management of subclinical and symptomatic varicoceles in male infertility remains challenging. Current guidelines focus on treating men with abnormal semen analyses, but a more precise approach to identify, stratify, and ...

Artificial Intelligence for Clinical Management of Male Infertility, a Scoping Review.

Current urology reports
PURPOSE OF REVIEW: Infertility impacts one in six couples worldwide, with male infertility contributing to approximately half of these cases. However, the causes of infertility remain incompletely understood, and current methods of clinical managemen...

The prediction of semen quality based on lifestyle behaviours by the machine learning based models.

Reproductive biology and endocrinology : RB&E
PURPOSE: To find the machine learning (ML) method that has the highest accuracy in predicting the semen quality of men based on basic questionnaire data about lifestyle behavior.

Multimodal distribution and its impact on the accurate assessment of spermatozoa morphological data: Lessons from machine learning.

Animal reproduction science
Objective assessment of sperm morphology is an essential component for assessing ejaculate quality. Due to economic limitations, investigators often divert to conducting observational studies instead of experimental ones, which provide the strongest ...

Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study.

Reproductive biology and endocrinology : RB&E
BACKGROUND: Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment o...

Machine learning approach to assess the association between anthropometric, metabolic, and nutritional status and semen parameters.

Asian journal of andrology
Many lifestyle factors, such as nutritional imbalance leading to obesity, metabolic disorders, and nutritional deficiency, have been identified as potential risk factors for male infertility. The aim of this study was to evaluate the relationship bet...