Fertility and sterility · 2026
High-throughput imaging cytometry paired with artificial intelligence identifies ultrarare sperm in men previously diagnosed with clinical azoospermia.
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Stocks Blair, Boyne Aidan, Oppenheimer Amelia, Salmon Katelynn, Kohn Taylor, Saffati Gal, Hernandez Beatriz, Badal Justin, Khera Mohit, Lipshultz Larry
Аннотация
OBJECTIVE: To determine whether a novel diagnostic platform, which pairs high-throughput imaging cytometry with artificial intelligence (AI)-assisted image analysis, can identify ultrarare sperm in ejaculatory samples deemed clinically azoospermic.
DESIGN: A retrospective cohort review of remnant biobanked ejaculatory samples processed by high-throughput imaging cytometry and AI-assisted image analysis.
SUBJECTS: Remnant ejaculatory samples (n = 148) biobanked from December 2024 to September 2025 at a single high-volume male infertility clinic.
EXPOSURE: Image-based flow cytometry and convolutional neural network (CNN) driven image analysis for diagnostic classification.
MAIN OUTCOME MEASURES: Diagnostic reclassification rate of remnant ejaculatory samples initially deemed clinically azoospermic using CNN-based morphologic assessment and corollary sperm specific protein expression.
RESULTS: Within the 83 azoospermic samples analyzed, 40 samples (48%) were found to have sperm using this high-fidelity diagnostic platform.
CONCLUSION: We propose a new classification of sperm concentration, microzoospermia, a sperm concentration so low that only advanced image-based search technologies can reliably identify ultrarare sperm.
DESIGN: A retrospective cohort review of remnant biobanked ejaculatory samples processed by high-throughput imaging cytometry and AI-assisted image analysis.
SUBJECTS: Remnant ejaculatory samples (n = 148) biobanked from December 2024 to September 2025 at a single high-volume male infertility clinic.
EXPOSURE: Image-based flow cytometry and convolutional neural network (CNN) driven image analysis for diagnostic classification.
MAIN OUTCOME MEASURES: Diagnostic reclassification rate of remnant ejaculatory samples initially deemed clinically azoospermic using CNN-based morphologic assessment and corollary sperm specific protein expression.
RESULTS: Within the 83 azoospermic samples analyzed, 40 samples (48%) were found to have sperm using this high-fidelity diagnostic platform.
CONCLUSION: We propose a new classification of sperm concentration, microzoospermia, a sperm concentration so low that only advanced image-based search technologies can reliably identify ultrarare sperm.