A Low Complexity and Cost Method to Diagnose Arterial Stenosis Using Lightwave Wearables

In this paper, we present a novel low cost and low complexity platform for the provisional diagnosis of vessels abnormalities, such as artery stenosis, aneurysms, etc. The proposed system is based on a low cost lightwave wearable device and advanced machine learning techniques to reduce the computational load and improve the accuracy of diagnosis. Specifically, in this work we focus on the image reconstruction in the case of an artery stenosis due to atheromatic plaque, where we briefly present the method and some preliminary results. The proposed platform can automatically provide a provisional diagnosis which can then be followed-up with further detailed and/or established imaging methods (e.g., Doppler ultrasound, Magnetic Resonance Angiography, etc) and treated promptly in order to minimize their likelihood of progression to higher levels of severity. This method may act as an adjunct to existing established screening programmes (e.g., arterial stenosis and aneurysm screening) or be used for new forms of population screening in the future.