Differentiating between obstructive and non‐obstructive azoospermia: A machine learning‐based approach
This study underscores the potential of machine learning to differentiate between azoospermia subtypes using readily available clinical data. However, further research is required to validate and refine the model before it can be applied clinically.
Authors
- Mohammad Ali Sadighi Gilani (ORCID: https://orcid.org/0000-0001-6168-8599)
- Abdolreza Haghpanah (ORCID: https://orcid.org/0000-0002-9728-7049)
- Nazanin Ayareh (ORCID: https://orcid.org/0000-0002-0027-4322)
- Dariush Irani (ORCID: https://orcid.org/0000-0002-3060-1718)
- Iman Shamohammadi (ORCID: https://orcid.org/0000-0002-9955-6257)
- Fatemeh Hosseini (ORCID: https://orcid.org/0000-0002-3843-7692)
- Ashkan Akbarzadeh
- Farid Sabahi Moghadam
Institutions
- Islamic Azad University Mahshahr (IR)
- Shiraz University of Medical Sciences (IR)
- Royan Institute (IR)
- Shariati Hospital (IR)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- BJUI Compass
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1002/bco2.493
- Citations
- 1
- Primary Topic
- Sperm and Testicular Function
- Type
- article
- Field-Weighted Citation Impact
- 1.70