HoloVein—Mixed-Reality Venipuncture Aid via Convolutional Neural Networks and Semi-Supervised Learning

Abstract

Attaining venous access is a common requirement for clinical care worldwide, with a non-negligible portion of cases often being categorized as ‘difficult intravenous access’. Such complications result in far-reaching consequences affecting clinicians and patients alike. We propose a mixed-reality-based vein detection and visual guidance system that provides several key advantages, including a wider field of view, flexible operating distance, and hands-free, intuitive usage compared to existing solutions. A semi-supervised learning approach was used in model training to circumvent dataset availability limitations. Quantitative evaluation showed that the semi-supervised approach improved vein detection performance and temporal consistency. The system was also implemented and trialed in a clinical setting to assess real-world usability. Initial, preliminary assessment of HoloVein by medical professionals in a clinical setting showed improvements in detection quality using the semi-supervised approach over the baseline model. This result was deemed to be promising from a clinical perspective and could set the stage for more widespread mixed-reality venipuncture guidance tools in the future.