[Artificial intelligence: to a better predictive strategy for testicular sperm extraction outcome in azoospermia]. / Intelligence artificielle : vers une meilleure stratégie de prédiction du résultat de la biopsie testiculaire dans un contexte d'azoospermie.

Azoospermia, defined as the absence of sperm in the semen, is found in 10-15 % of infertile patients. Two-thirds of these cases are caused by impaired spermatogenesis, known as non-obstructive azoospermia (NOA). In this context, surgical sperm extraction using testicular sperm extraction (TESE) is the best option and can be offered to patients as part of fertility preservation, or to benefit from in vitro fertilization. The aim of the preoperative assessment is to identify the cause of NOA and evaluate the status of spermatogenesis. Its capacity to predict TESE success remains limited. As a result, no objective and reliable criteria are currently available to guide professionals on the chances of success and enable them to correctly assess the benefit-risk balance of this procedure. Artificial intelligence (AI), a field of research that has been rapidly expanding in recent years, has the potential to revolutionize medicine by making it more predictive and personalized. The aim of this review is to introduce AI and its key concepts, and then to examine the current state of research into predicting the success of TESE.

Impact of testicular cancer on sperm small non-coding RNA signature: a pilot study.

Testicular germ cell tumours (TGCTs) are the most common tumours in young adults of European ancestry. The high heritability and the constantly increased incidence, which has doubled over the last 20 years, strongly suggest that both genetic and environmental factors are likely to shape the TGCT susceptibility. While genome-wide association studies have identified loci associated with TGCT susceptibility, the role played by environmental molecular vectors in TGCT susceptibility remains unclear. Evidence shows that sperm non-coding RNAs provide a good vision of the environmental stresses experienced by men. Here, to determine whether TGCT impacts the abundance of specific non-coding RNAs in sperm, small RNA deep sequencing analysis of sperm of 25 men aged between 19 and 42 years, diagnosed with (n = 16) or without (n = 9) TGCT was performed. The primary analysis showed no statistical significance in the sncRNA population between the TGCT and non-TGCT groups. However, when sperm physiological parameters were considered to look for differentially expressed sncRNA, we evidenced 11 differentially expressed sncRNA between patients and control which allow a clear discrimination between control and TGCT samples after Hierarchical Clustering analysis. Together, these findings indicate that sperm small non-coding RNAs abundance may have the potential for diagnosing men with TGCT. However, specific care should be taken regarding sperm physiological parameters of the TGCT patients. Hence, larger studies are needed to confirm our findings and to determine whether such a signature associates with the risks to develop TGCT.

A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study.

BACKGROUND: Testicular sperm extraction (TESE) is an essential therapeutic tool for the management of male infertility. However, it is an invasive procedure with a success rate up to 50%. To date, no model based on clinical and laboratory parameters is sufficiently powerful to accurately predict the success of sperm retrieval in TESE. OBJECTIVE: The aim of this study is to compare a wide range of predictive models under similar conditions for TESE outcomes in patients with nonobstructive azoospermia (NOA) to identify the correct mathematical approach to apply, most appropriate study size, and relevance of the input biomarkers. METHODS: We analyzed 201 patients who underwent TESE at Tenon Hospital (Assistance Publique-Hôpitaux de Paris, Sorbonne University, Paris), distributed in a retrospective training cohort of 175 patients (January 2012 to April 2021) and a prospective testing cohort (May 2021 to December 2021) of 26 patients. Preoperative data (according to the French standard exploration of male infertility, 16 variables) including urogenital history, hormonal data, genetic data, and TESE outcomes (representing the target variable) were collected. A TESE was considered positive if we obtained sufficient spermatozoa for intracytoplasmic sperm injection. After preprocessing the raw data, 8 machine learning (ML) models were trained and optimized on the retrospective training cohort data set: The hyperparameter tuning was performed by random search. Finally, the prospective testing cohort data set was used for the model evaluation. The metrics used to evaluate and compare the models were the following: sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and accuracy. The importance of each variable in the model was assessed using the permutation feature importance technique, and the optimal number of patients to include in the study was assessed using the learning curve. RESULTS: The ensemble models, based on decision trees, showed the best performance, especially the random forest model, which yielded the following results: AUC=0.90, sensitivity=100%, and specificity=69.2%. Furthermore, a study size of 120 patients seemed sufficient to properly exploit the preoperative data in the modeling process, since increasing the number of patients beyond 120 during model training did not bring any performance improvement. Furthermore, inhibin B and a history of varicoceles exhibited the highest predictive capacity. CONCLUSIONS: An ML algorithm based on an appropriate approach can predict successful sperm retrieval in men with NOA undergoing TESE, with promising performance. However, although this study is consistent with the first step of this process, a subsequent formal prospective multicentric validation study should be undertaken before any clinical applications. As future work, we consider the use of recent and clinically relevant data sets (including seminal plasma biomarkers, especially noncoding RNAs, as markers of residual spermatogenesis in NOA patients) to improve our results even more.