[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.
artificial intelligence azoospermia infertility

Journal

Annales de biologie clinique
ISSN: 1950-6112
Titre abrégé: Ann Biol Clin (Paris)
Pays: France
ID NLM: 2984690R

Informations de publication

Date de publication:
01 Apr 2024
Historique:
medline: 4 5 2024
pubmed: 4 5 2024
entrez: 4 5 2024
Statut: aheadofprint

Résumé

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.

Identifiants

pubmed: 38702888
pii: abc.2024.1882
doi: 10.1684/abc.2024.1882
doi:

Types de publication

English Abstract Journal Article

Langues

fre

Sous-ensembles de citation

IM

Auteurs

Guillaume Bachelot (G)

Sorbonne Université, Faculté de Médecine, Saint Antoine Research Center, INSERM UMR 938, 27 rue Chaligny, Paris, France, Service de Biologie de la Reproduction-CECOS, Hôpital Tenon, AP-HP, Sorbonne Université, 75020 Paris, France.

Anna Ly (A)

Service de Biologie de la Reproduction-CECOS, Hôpital Tenon, AP-HP, Sorbonne Université, 75020 Paris, France.

Diane Rivet-Danon (D)

Service de Biologie de la Reproduction-CECOS, Hôpital Tenon, AP-HP, Sorbonne Université, 75020 Paris, France.

Nathalie Sermondade (N)

Sorbonne Université, Faculté de Médecine, Saint Antoine Research Center, INSERM UMR 938, 27 rue Chaligny, Paris, France, Service de Biologie de la Reproduction-CECOS, Hôpital Tenon, AP-HP, Sorbonne Université, 75020 Paris, France.

Valentine Frydman (V)

Service d'Urologie, Hôpital Tenon, AP-HP, Sorbonne Université, 75020 Paris, France.

Antonin Lamazière (A)

Sorbonne Université, Faculté de Médecine, Saint Antoine Research Center, INSERM UMR 938, 27 rue Chaligny, Paris, France, Département de Métabolomique Clinique, Hôpital Saint Antoine, AP-HP, Sorbonne Université, 75012 Paris, France.

Rahaf Haj Hamid (RH)

Service de Biologie de la Reproduction-CECOS, Hôpital Tenon, AP-HP, Sorbonne Université, 75020 Paris, France.

Rachel Levy (R)

Sorbonne Université, Faculté de Médecine, Saint Antoine Research Center, INSERM UMR 938, 27 rue Chaligny, Paris, France, Service de Biologie de la Reproduction-CECOS, Hôpital Tenon, AP-HP, Sorbonne Université, 75020 Paris, France.

Charlotte Dupont (C)

Sorbonne Université, Faculté de Médecine, Saint Antoine Research Center, INSERM UMR 938, 27 rue Chaligny, Paris, France, Service de Biologie de la Reproduction-CECOS, Hôpital Tenon, AP-HP, Sorbonne Université, 75020 Paris, France.

Classifications MeSH