This paper is in the following e-collection/theme issue:

Theme Issue: Reproductive Informatics (61) New Methods (1134) Obstetrics, Gynecology, and Reproductive Medicine (329) Decision Support for Health Professionals (1539)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44047, first published .
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

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

Authors of this article:

Guillaume Bachelot1, 2, 3 Author Orcid Image ;   Ferdinand Dhombres3 Author Orcid Image ;   Nathalie Sermondade1, 2 Author Orcid Image ;   Rahaf Haj Hamid2 Author Orcid Image ;   Isabelle Berthaut2 Author Orcid Image ;   Valentine Frydman4 Author Orcid Image ;   Marie Prades2 Author Orcid Image ;   Kamila Kolanska1, 5 Author Orcid Image ;   Lise Selleret4 Author Orcid Image ;   Emmanuelle Mathieu-D’Argent1, 5 Author Orcid Image ;   Diane Rivet-Danon2 Author Orcid Image ;   Rachel Levy1, 2 Author Orcid Image ;   Antonin Lamazière1, 6 Author Orcid Image ;   Charlotte Dupont1, 2 Author Orcid Image
Article Authors Cited by (18) Tweetations (4) Metrics

Journals

  1. Panner Selvam M, Moharana A, Baskaran S, Finelli R, Hudnall M, Sikka S. Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis. Medicina 2024;60(2):279 View
  2. Schmeis Arroyo V, Iosa M, Antonucci G, De Bartolo D. Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature. Healthcare 2024;12(7):781 View
  3. Olivennes J, Ogouma Aworet L, Ly A, Dupont C, Bachelot G. Azoospermie avec biopsie testiculaire positive : à propos d’un cas. Revue Francophone des Laboratoires 2024;2024(563):73 View
  4. Naik N, Roth B, Lundy S. Artificial Intelligence for Clinical Management of Male Infertility, a Scoping Review. Current Urology Reports 2025;26(1) View
  5. Kaltsas A, Stavros S, Kratiras Z, Zikopoulos A, Machairiotis N, Potiris A, Dimitriadis F, Sofikitis N, Chrisofos M, Zachariou A. Predictors of Successful Testicular Sperm Extraction: A New Era for Men with Non-Obstructive Azoospermia. Biomedicines 2024;12(12):2679 View
  6. Colaco S, Narad P, Singh A, Gupta P, Choudhury A, Sengupta A, Modi D. FertilitY Predictor—a machine learning-based web tool for the prediction of assisted reproduction outcomes in men with Y chromosome microdeletions. Journal of Assisted Reproduction and Genetics 2025;42(2):473 View
  7. Jamalirad H, Jajroudi M, Khajehpour B, Sadighi Gilani M, Eslami S, Sabbaghian M, Vakili Arki H. AI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review. Human Reproduction Open 2025;2025(1) View
  8. Panken E, Patel A, Schammel J, Dubin J. Man and machine: exploring the intersection of artificial intelligence and men's health. Current Opinion in Urology 2025;35(3):236 View
  9. Nasrallah O, Al Hattab M, Bachir B. Artificial intelligence and varicocelectomy: A new horizon for patient management? A narrative review. UroPrecision 2025;3(2):61 View
  10. Huang S, Luo Q, Li X, Liu Y, Wei J, Wang S, Qiu P, Li C. Homotherapy for heteropathy of chronic kidney disease and oligoasthenozoospermia through regulating SIRT1/NF-κB pathway by Shenqi pills. Frontiers in Pharmacology 2025;16 View
  11. Soczyńska J, Gawełczyk W, Papierkowska J, Muzyka A, Majcherczyk K, Obrycka P, Żołyniak M, Woźniak S. The future of embryo engineering and fertility research in interdisciplinary collaboration. Frontiers in Cell and Developmental Biology 2025;13 View
  12. Xi Y, Zhang B, Zhang Y, Zhao L, Liu D, Mao J, Tang W, Zhang H, Lin H, Wang X, Ren P, Tang Y, Yang Y, Hong K, Guo J, Zhang Z, Jiang H. Machine learning–based personalized prediction of sperm retrieval in patients with non‐obstructive azoospermia prior to microdissection testicular sperm extraction: A multi‐center cohort study. Andrology 2026;14(1):237 View
  13. Santi D, Pozza C, Spaggiari G, Gianfrilli D, Sbardella E, Paoli D, Roli L, De Santis M, Bonomi M, Trenti T, Isidori A, Simoni M. Machine Learning Evaluation of Semen Analysis Could Reveal New Infertility-Related Markers: A Pilot Study. The World Journal of Men's Health 2025;43 View
  14. Wang H, He J, Guo Y, Zhou L, Li S, He Z, Jing Q, Wang P, Qu L, Gao J, Liu G, Wang H, Yang Y, Ge P, Zhang J, Wang X, Lv M, Chen H, Zhou D. Development of a predictive model and nomogram in sperm retrieval rate based on testicular pathological morphometric parameters in non-obstructive azoospermia patients: a multi-center study. Reproductive Biology and Endocrinology 2025 View

Books/Policy Documents

  1. Zachariou A, Giannakis I, Baltogiannis D, Kaltsas A, Zikopoulos A, Stavros S, Kojovic V, Pantou A, Takenaka A, Sofikitis N. Integrative Male Reproductive Health - Risk, Mechanisms, and Interventions. View
  2. Reese V, Santare J, Lee R, Maddahi Y, Verges D. Artificial Intelligence in Medicine and Surgery - An Exploration of Current Trends, Potential Opportunities, and Evolving Threats, Volume 3 [Working Title]. View
  3. Bigdeli A, Kanakasabapathy M, Parandakh A, Shafiee H. Robotics and Artificial Intelligence for Reproductive Medicine. View
  4. Torres-Anguiano J, Shin D. Reference Module in Biomedical Sciences. View