Semiquantitative Determination of Thiocyanate in Saliva Through Colorimetric Assays: Design of CNN Architecture via Input-Aware NAS

This article presents a novel method enabling point-of-care (POC) testing of thiocyanate concentration in saliva. Thiocyanate is an important biological marker; its levels are linked with diseases such as cancer and neurodegeneration. Hence, monitoring this marker frequently can positively impact users’ lives. In the proposed setup, the goal is a semiquantitative reading of thiocyanate concentration from colorimetric assays in solution; the user-friendly, yet accurate readout procedure relies on a smartphone camera and is designed to be robust against moderate changes in indoor lighting conditions. The readout procedure exploits the capabilities of convolutional neural networks (CNNs) to fully profit from a setup involving a custom color chart and the assay vial. Thus, a data-driven strategy is adopted to deal with color distortions caused both by lighting conditions and by postprocessing operations embedded in the smartphone camera. A neural architecture search (NAS) procedure explicitly tuned for the problem at hand drove the design of the custom CNN architecture. The method has been tested using a collection of real-world data and compared with existing approaches. The results presented in this article show an increase in accuracy up to about 14% with respect to state-of-the-art methods.