Leveraging synthetic imagery and YOLOv8 for a novel colorimetric approach to paper-based point-of-care male fertility testing

Abstract

The development of paper-based systems has revolutionized point-of-care (POC) applications by enabling rapid, robust, accurate and sensitive biochemical analysis, infectious disease diagnosis, and fertility monitoring, in particular, in male fertility monitoring, offering portable, cost-effective solutions compared to traditional methods. This innovation addresses high costs and limited accessibility of male fertility testing in resource-poor settings. Male infertility, a significant issue globally, often faces stigma, hindering men from seeking care. This study introduces a novel approach to male fertility testing using colorimetric analysis of paper-based assays, enhanced by synthetic imagery and the YOLOv8 (You Only Look Once) object detection algorithm. Synthetic imagery was employed to train and fine-tune YOLOv8, enhancing its capability to accurately detect color changes in paper-based tests. This colorimetric detection leverages smartphone imaging, making it both accessible and scalable. Initial experiments demonstrate that YOLOv8’s precision and efficiency, when combined with synthetic data, significantly enhance the system's ability to recognize and analyze colorimetric signals, positioning it as a promising tool for male fertility POC diagnostics. In our study, we evaluated 39 semen samples for pH and sperm count using standard clinical tests, comparing these results with a novel paper-based semen analysis kit. This kit utilizes reaction zones that exhibit color changes when exposed to semen samples, with images captured using a smartphone under varied lighting conditions. Despite a limited number of images, our synthetically trained YOLOv8 model achieved an accuracy of 0.86, highlighting its potential to improve the reliability of colorimetric analysis for both home and clinical use.