Smartphone-Based Diagnosis of Parasitic Infections With Colorimetric Assays in Centrifuge Tubes

• Published in: IEEE access Vol. 7; pp. 185677 - 185686
• Main Authors: Escobedo, Pablo, Erenas, Miguel M., Martinez Olmos, Antonio, Carvajal, Miguel A., Tabraue Chavez, Mavys, Luque Gonzalez, M. Angelica, Diaz-Mochon, Juan J., Pernagallo, Salvatore, Capitan-Vallvey, Luis Fermin, Palma, Alberto J.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrays; Bioinformatics; Centrifuges; Color; colorimetric assay; Colorimetry; Diagnosis; Diagnostic systems; DNA; Electron tubes; Error correction; Genomics; Image color analysis; Image processing; Livestock; Parasitic diseases; Plastics; Probes; Smartphone-based platform; Smartphones; Three dimensional printing; Tubes
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2961230

A smartphone-based platform for the diagnosis of parasitic infections has been developed, tested and validated. The system is capable of making automatic and accurate analysis of millimetric colorimetric arrays in centrifuge collection tubes, which are well established tools used in clinical analysis. To that end, an Android-based software application has been developed, making use of the smartphone rear camera, enabling precise image processing of the colorimetric spot arrays. A low-cost plastic accessory has been developed using 3D-printing to provide controlled illumination, fixed sample positioning and cell phone attachment. The platform was then tested repeatedly for its size detection, edge blurriness and colour detection capabilities. A minimum spot radius of 175 μm is detectable when using the developed app, with a tolerance of 15%, corresponding to 0.25% of the area where the spot array is printed. Spot edge definition has been studied up to 40% of blurriness, resulting in a low average percentage error of 1.24%. Colour detection follows the well-known Gamma correction function. Finally, the whole platform was tested and validated using real DNA to analyse for accurate discrimination of Trypanosomatid species, which are responsible for devastating diseases in humans and livestock. The smartphone-based platform can be further extended to other clinical analysis. Its simplicity and reliable performance mean it can be used in remote, limited-resource settings by relatively unskilled technicians/nurses, where diagnostic laboratories are sparsely distributed. The results can however be sent easily via the smartphone to medical experts as well as government health agencies.