Vein Detection using Infrared Technology and Machine Learning for Cannulation

Intravenous cannulation is the most important phase in medical practices. With limited literature available in this field of health care, this project encompasses the development of a comprehensive solution, combining the use of compact hardware equipment, image enhancement techniques and machine learning algorithms to improve identification of veins and characteristics of patients associated with faint intravenous access. A specialized imaging system, equipped with infrared or near-infrared light sources and NoIR camera, is employed to capture vein patterns beneath the skin's surface. These images are subsequently subjected to image enhancement and machine learning techniques to optimize vein identification, improving the quality and reliability of the acquired data. The utilization of this tool will aid healthcare workers enhance their ability to address and minimize risks associated with patients.