The review article titled“Integrating artificial intelligence in surgical sperm retrieval techniques: A narrative review”, was published on November 11, 2024 in UroPrecision.

NOA represents a severe form of male infertility, characterized by the absence of sperm in the ejaculate due to impaired spermatogenesis. SSR procedures, such as microdissection testicular sperm extraction (micro-TESE), are crucial for these patients as they offer the chance to father biological children through assisted reproductive technologies. However, the success of SSR is influenced by multiple factors, including genetic background, endocrine optimization, testicular histopathology, and surgical expertise. This narrative review explores the role of AI in enhancing SSR outcomes for NOA patients.

AI has revolutionized healthcare by developing computer systems capable of performing complex tasks that typically require human intelligence. In the context of male infertility, AI has evolved from predictive models to advanced image processing techniques, significantly improving sperm retrieval rates and accuracy. Predictive models using machine learning algorithms, such as artificial neural networks and random forest models, have been developed to forecast sperm retrieval success based on preoperative patient data. These models help in counseling patients and guiding clinical decisions.

In terms of sperm detection, AI has advanced from traditional image processing techniques to sophisticated deep learning algorithms, such as convolutional neural networks. These algorithms can analyze low-magnification microscopy images of testicular tissue to identify rare sperm, significantly improving detection speed and accuracy compared to manual identification by embryologists. For instance, AI models have demonstrated the ability to detect sperm in less than a thousandth of the time taken by trained embryologists, with higher recall rates.

The integration of AI in SSR procedures also addresses challenges associated with specimen variability and laboratory processing. AI-driven models can reduce the risk of false-negative results, which may arise due to human error or limited laboratory capabilities. By automating the detection process, AI ensures more consistent and reliable outcomes, enhancing the overall success rate of SSR.

Despite these advancements, the application of AI in SSR faces several challenges. The complexity of testicular tissue samples and the need for extensive training datasets pose significant hurdles. Additionally, ethical considerations, such as the impact of AI-selected sperm on offspring health and the financial burden of implementing AI technologies, must be addressed to ensure their universal applicability.

In conclusion, AI has emerged as a transformative tool in the management of male infertility, particularly in improving SSR outcomes for NOA patients. As AI technologies continue to advance, they hold the potential to further refine sperm retrieval processes, enhance diagnostic accuracy, and ultimately increase the chances of successful assisted reproductive technologies outcomes. Future directions include expanding datasets, improving algorithm robustness, and conducting more validation studies to ensure the generalizability of AI models in diverse clinical settings. The integration of AI into reproductive medicine heralds a new era of precision and efficiency, offering hope to couples facing the challenges of male infertility.

DOI: 10.1002/uro2.100