O-136 Artificial intelligence to assist in surgical sperm detection and isolation

Abstract Study question Can an artificial intelligence (AI) improve the speed and accuracy of identifying sperm in complex testicular tissue samples? Summary answer Trained AI can identify sperm in real-time instantly with higher accuracy, not only reducing strain on embryologists but increasing sample coverage in a shorter time. What is known already Non-obstructive azoospermia (NOA) is a form of severe male-factor infertility, affecting nearly 5% of infertile couples seeking treatment. Isolating sperm from macerated testicular tissue for intracytoplasmic sperm injection (ICSI) has changed marginally in the last two decades, and still requires embryologists to search arduously through a background of collateral cells including red blood cells (RBC’s), white blood cells (WBC’s), leydig, sertoli and epithelial cells, causing fatigue and reducing sample coverage. Image analysis using trained AI can instantly identify shapes and forms and thus presents itself as a candidate to dramatically reduce processing times in surgical sperm cases. Study design, size, duration This proof-of-concept consists of two phases over 5 months. A training phase using 7 azoospermic patients to provide samples to train a convolutional neural network with manual annotation. Secondly, a side-by-side live test with an embryologist versus the AI model comparing time taken and accuracy of sperm identification, and precision of identifying sperm in macerated tissue samples (false positives and false negatives). Results were analysed using Mann-Whitney U-test. Differences were considered significant when p-value