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

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

In the context of azoospermia, testicular sperm extraction (TESE) can be proposed to obtain mature germ cells (ie, spermatozoa) for in vitro fertilization with intracytoplasmic sperm injection (ICSI) [1-3]. Several surgical techniques are available for this, including conventional surgical TESE (c TESE) and microsurgical TESE (micro TESE), the latter of which requires the use of an operating microscope to visualize the seminiferous tubules that are most likely to contain complete spermatogenesis [4].

Guillaume Bachelot, Ferdinand Dhombres, Nathalie Sermondade, Rahaf Haj Hamid, Isabelle Berthaut, Valentine Frydman, Marie Prades, Kamila Kolanska, Lise Selleret, Emmanuelle Mathieu-D’Argent, Diane Rivet-Danon, Rachel Levy, Antonin Lamazière, Charlotte Dupont

J Med Internet Res 2023;25:e44047