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Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10782130 | PMC |
http://dx.doi.org/10.5534/wjmh.230050 | DOI Listing |
Arch Esp Urol
August 2025
Department of Urology, National Taiwan University Hospital, 10002 Taipei, Taiwan.
Objective: Conventional penile venous surgery for erection restoration and surgery for penile augmentation have been controversial. Based on de novo penile fibrovascular assembly, we report innovative penile venous stripping (PVS) and factual penile girth enhancement (FPGE).
Methods: From 2013 to 2023, refractory impotence and dysmorphia prompted 31 patients to seek PVS and FPGE, and all of them were confirmed with veno-occlusive dysfunction.
Reprod Sci
September 2025
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
The genetic etiology is unknown for 30-40% of men with congenital bilateral absence of the vas deferens (CBAVD) and 70% of those with congenital unilateral absence of the vas deferens (CUAVD). The study aimed to investigate the genetic etiology of CBAVD/CUAVD, both with and without renal anomalies, in individuals who are negative for CFTR pathogenic variants. We included 19 cases of congenital absence of vas deferens (CAVD) that were negative for CFTR variants on Sanger sequencing.
View Article and Find Full Text PDFCell Discov
September 2025
Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China.
Adverse intrauterine environments, such as hyperglycemia, impair sexual reproduction and species continuity, yet the underlying mechanisms remain poorly understood. In this study, we demonstrated that intrauterine hyperglycemia significantly disrupted primordial germ cell (PGC) development, especially in female offspring, thus reducing fertility. Using Oct4-EGFP transgenic mice with intrauterine hyperglycemia exposure, we revealed that hyperglycemia compromised sexually specific chromatin accessibility and DNA methylation reprogramming during PGC development.
View Article and Find Full Text PDFInt J Vitam Nutr Res
July 2025
Department of Urology and Andrology, The First Affiliated Hospital, Sun Yat-sen University, 510080 Guangzhou, Guangdong, China.
Background: Obesity, a prevalent global health issue, is associated with testosterone deficiency (TD). A body shape index (ABSI) provides a more precise assessment of obesity and visceral fat, but its relationship with testosterone remains unclear. This study aimed to explore the association between ABSI and testosterone levels leading to TD.
View Article and Find Full Text PDFAndrology
September 2025
Department of Urology, Peking University First Hospital, Beijing, China.
Background: Non-obstructive azoospermia represents the most severe form of male infertility. The heterogeneous nature of focal spermatogenesis within the testes of non-obstructive azoospermia patients poses significant challenges for accurately predicting sperm retrieval rates.
Objectives: To develop a machine learning-based predictive model for estimating sperm retrieval rates in patients with non-obstructive azoospermia.