Automated human sperm analysis systems and methods utilize deep learning networks enhanced with artificial intelligence.

Abstract

The invention relates to a computer-based system and method for semi-automatic analysis of human sperm. The system communicates a standard microscope to a host computer via a USB-connected digital imaging device, processes the live microscopic video stream, and generates a diagnostic report compliant with WHO guidelines, stored in a secure cloud storage service. The system automatically performs quantitative assessment of sperm concentration, tracks individual sperm motility patterns, and classifies morphological abnormalities. An adaptive keyframe extraction module determines the optimal time-sampling rate by calculating a composite score derived from keyframe similarity and motility index. A first-order deep neural network quantifies sperm concentration across five standardized microscopic observation fields.The second deep neural network, enhanced with a Kalman filter operating on a tracking-by-detection model, assigns continuously maintained tracking identifiers to each sperm and calculates kinetic parameters to classify each sperm into one of four motility groups according to WHO standards. The third deep neural network, tuned through parameter fine-tuning, performs pixel-by-pixel semantic segmentation of the head, midsection, and tail of each detected sperm across seven morphological classes. A field drift detection and mitigation mechanism is implemented to eliminate global motion spurious effects from the kinetic tracking process. The system is adapted to receive and process minimally prepared clinical samples to output diagnostic data related to male reproductive health.