Urinalysis dipsticks were designed to revolutionize urine-based medical diagnosis. They are cheap, extremely portable, and have multiple assays patterned on a single platform. They were also meant to be incredibly easy to use. Unfortunately, there are many aspects in both the preparation and the analysis of the dipsticks that are plagued by user error. This high error is one reason that dipsticks have failed to flourish in both the at-home market and in low-resource settings. Sources of error include: inaccurate volume deposition, varying lighting conditions, inconsistent timing measurements, and misinterpreted color comparisons. We introduce a novel manifold and companion software for dipstick urinalysis that eliminates the aforementioned error sources. A micro-volume slipping manifold ensures precise sample delivery, an opaque acrylic box guarantees consistent lighting conditions, a simple sticker-based timing mechanism maintains accurate timing, and custom software that processes video data captured by a mobile phone ensures proper color comparisons. We show that the results obtained with the proposed device are as accurate and consistent as a properly executed dip-and-wipe method, the industry gold-standard, suggesting the potential for this strategy to enable confident urinalysis testing. Furthermore, the proposed all-acrylic slipping manifold is reusable and low in cost, making it a potential solution for at-home users and low-resource settings.
Fusing a lower resolution color image with a higher resolution monochrome image is a common practice in medical imaging. By incorporating spatial context and/or improving the signal-to-noise ratio, it provides clinicians with a single frame of the most complete information for diagnosis. In this paper, image fusion is formulated as a convex optimization problem that avoids image decomposition and permits operations at the pixel level. This results in a highly efficient and embarrassingly parallelizable algorithm based on widely available robust and simple numerical methods that realizes the fused image as the global minimizer of the convex optimization problem.