Semi-quantitative determination of thiocyanate in saliva through colorimetric assays: design of CNN architecture via input-aware NAS

This paper presents a novel method enabling point-of-care 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 semi-quantitative 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 post-processing 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 paper show an increase in accuracy up to about 14% with respect to state-of-the-art methods.