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 in current studies include small sample sizes and lack of validation.
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 for having a baby. This can help them make better decisions about treatment.
Methodology
A systematic scoping review was conducted following PRISMA-ScR guidelines, covering PubMed and Scopus databases from 2013 to May 2024, including studies on patients with NOA where AI-based models were used for predicting m-TESE outcomes.
Potential Biases
All included studies demonstrated a low risk of bias and minimal concerns regarding applicability.
Limitations
The review includes heterogeneity of included research, potential publication bias, reliance on only two databases, and absence of a meta-analysis.
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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