To determine whether a novel diagnostic platform which pairs high-throughput imaging cytometry with Artificial Intelligence (AI) assisted image analysis can identify ultra-rare sperm in ejaculatory samples deemed clinically azoospermic.
A retrospective cohort review of remnant biobanked ejaculatory samples processed by high-throughput imaging cytometry and AI assisted image analysis.
Remnant ejaculatory samples (n=148) biobanked from December 2024 to September 2025 at a single high-volume male infertility clinic.
Image-based Flow Cytometry and Convolutional Neural Network (CNN) driven image analysis for diagnostic classification.
Diagnostic reclassification rate of a remnant ejaculatory samples initially deemed clinically azoospermic using CNN-based morphologic assessment and corollary sperm specific protein expression.
Within the 83 azoospermic samples analyzed, 40 samples (48%) were found to have sperm using this high-fidelity diagnostic platform.
We propose a new classification of sperm concentration - microzoospermia - a sperm concentration so low that only advanced image-based search technologies can reliably identify ultra-rare sperm.
Fertility and sterility. 2026 Apr 24 [Epub ahead of print]
Blair Stocks, Aidan Boyne, Amelia Oppenheimer, Katelynn Salmon, Taylor Kohn, Gal Saffati, Beatriz Hernandez, Justin Badal, Mohit Khera, Larry Lipshultz
Scott Department of Urology, Division of Male Reproductive Medicine and Surgery, Baylor College of Medicine, Houston, TX., School of Medicine, Baylor College of Medicine, Houston, TX., Scott Department of Urology, Division of Male Reproductive Medicine and Surgery, Baylor College of Medicine, Houston, TX. Electronic address: .