AI Models and Testicular Sperm Extraction for Male Infertility
Author Information
Author(s): Hossein Jamalirad, Mahdie Jajroudi, Bahareh Khajehpour, Mohammad Ali Sadighi Gilani, Saeid Eslami, Marjan Sabbaghian, Hassan Vakili Arki
Primary Institution: Mashhad University of Medical Sciences, Mashhad, Iran
Hypothesis
How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Conclusion
AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
Supporting Evidence
- AI models can analyze various factors to predict sperm retrieval success.
- Machine learning techniques have shown promise in improving predictions for sperm retrieval.
- Limitations include small sample sizes and variability in study designs.
Takeaway
Doctors can use AI to help predict if a man with a specific type of infertility will be able to get sperm from his testicles, which can help couples have babies.
Methodology
A systematic scoping review was conducted following PRISMA-ScR guidelines, covering PubMed and Scopus databases from 2013 to May 2024.
Potential Biases
All included studies demonstrated a low risk of bias and minimal concerns regarding applicability.
Limitations
The review includes heterogeneity of included studies, potential publication bias, and reliance on only two databases, which may limit the findings.
Participant Demographics
The review covered information from 11,636 patients with NOA who had undergone m-TESE.
Digital Object Identifier (DOI)
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