Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 4, 2022
Date Accepted: Apr 7, 2023
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Machine learning-based prediction of testicular sperm extraction: A comparison of different models.
ABSTRACT
Background:
Testicular sperm extraction (TESE) is an essential therapeutic tool for the male infertility management. However, it is an invasive procedure and is successful in up to 50%. Until now, no model, based on the clinical and laboratory parameters, is sufficiently powerful to accurately predict the success of sperm retrieval in TESE.
Objective:
The objective of this study is to compare a wide range of predictive models under similar conditions for TESE outcomes in patients with non-obstructive azoospermia (NOA) to identify the correct mathematical approach to apply thereto, as well as the most appropriate study size and the relevance of input biomarkers.
Methods:
Two-hundred and one patients who underwent TESE at Tenon Hospital (AP-HP, Sorbonne University, Paris) distributed in a retrospective training cohort of 175 patients (2012 to April 2021) and a prospective testing cohort (May 2021 to December 2021) of 26 patients were analyzed. Eight machine learning (ML) models were evaluated (temporal validation) and compared.
Results:
The best model was the random forest (RF): (Area under curve) AUC = 89.6%, sensitivity = 100%, and specificity = 69,2%. Moreover, a study size of 120 patients seemed to be sufficient to properly exploit the preoperative data during the modeling process. Furthermore, inhibin B and a history of varicoceles exhibited the highest predictive capacity.
Conclusions:
Using the appropriate approach, an ML algorithm can accurately predict successful sperm retrieval in men with NOA under-going TESE. However, it will be necessary to confirm and validate these results in a multicentric prospective study prior to practical use. The applicability of seminal plasma biomarkers, especially noncoding RNAs, as markers of residual spermatogenesis in NOA patients also requires further study.
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