Cell biology

In vitro fertilisation

Biology

Human fertilization

Sperm

Testicular sperm extraction

Azoospermia

Semen

Genetics

Embryo

Anatomy

Intracytoplasmic sperm injection

Infertility

Andrology

Medicine

Sperm Retrieval

Pregnancy

Fertility and Sterility

Objective To develop a machine learning algorithm to detect rare human sperm in semen and microsurgical testicular sperm extraction (microTESE) samples using bright-field (BF) microscopy for nonobstructive azoospermia patients. Design Spermatozoa were collected from fertile men. Testis biopsies were collected from microTESE samples determined to be clinically negative for sperm. A convolutional neural network based on the U-Net architecture was trained using 35,761 BF image patches with fluorescent ground truth image pairs to segment sperm. The algorithm was validated using 7,663 image patches. The algorithm was tested using 7,663 image patches containing abundant sperm, as well as 7,985 image patches containing rare sperm. Setting In vitro fertilization center and university laboratories. Patient(s) Normospermic and nonobstructive azoospermia patients. Intervention(s) None. Main Outcome Measure(s) Precision (positive predictive value [PPV]), recall (sensitivity), and F1-score of detected sperm locations. Result(s) For sperm-only samples, our algorithm achieved 91% PPV, 95.8% sensitivity, and 93.3% F1-score at ×10 magnification. For dissociated microTESE samples doped with an abundant quantity of sperm, our algorithm achieved 84.0% PPV, 72.7% sensitivity, and 77.9% F1-score. For dissociated microTESE samples doped with rare sperm, our algorithm achieved 84.4% PPV, 86.1% sensitivity, and 85.2% F1-score. Conclusion(s) Rare sperm can be detected in patients’ testis biopsy samples for potential subsequent use in in vitro fertilization–intracytoplasmic sperm injection. A machine learning algorithm can use BF images at ×10 magnification to accurately detect sperm locations using automated imaging. To develop a machine learning algorithm to detect rare human sperm in semen and microsurgical testicular sperm extraction (microTESE) samples using bright-field (BF) microscopy for nonobstructive azoospermia patients. Spermatozoa were collected from fertile men. Testis biopsies were collected from microTESE samples determined to be clinically negative for sperm. A convolutional neural network based on the U-Net architecture was trained using 35,761 BF image patches with fluorescent ground truth image pairs to segment sperm. The algorithm was validated using 7,663 image patches. The algorithm was tested using 7,663 image patches containing abundant sperm, as well as 7,985 image patches containing rare sperm. In vitro fertilization center and university laboratories. Normospermic and nonobstructive azoospermia patients. None. Precision (positive predictive value [PPV]), recall (sensitivity), and F1-score of detected sperm locations. For sperm-only samples, our algorithm achieved 91% PPV, 95.8% sensitivity, and 93.3% F1-score at ×10 magnification. For dissociated microTESE samples doped with an abundant quantity of sperm, our algorithm achieved 84.0% PPV, 72.7% sensitivity, and 77.9% F1-score. For dissociated microTESE samples doped with rare sperm, our algorithm achieved 84.4% PPV, 86.1% sensitivity, and 85.2% F1-score. Rare sperm can be detected in patients’ testis biopsy samples for potential subsequent use in in vitro fertilization–intracytoplasmic sperm injection. A machine learning algorithm can use BF images at ×10 magnification to accurately detect sperm locations using automated imaging.

Automated rare sperm identification from low-magnification microscopy images of dissociated microsurgical testicular sperm extraction samples using deep learning