Smartphone-Based Diagnosis of Parasitic Infections With Colorimetric Assays in Centrifuge Tubes
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AuthorEscobedo, Pablo; Erenas, Miguel M; Martínez Olmos, Antonio; Carvajal Rodríguez, Miguel Ángel; Tabraue Chávez, Mavys; Luque González, Angélica; Díaz-Mochón, Juan J.; Pernagallo, Salvatore; Capitán Vallvey, Luis Fermín; Palma López, Alberto José
Smartphone-based platformParasitic diseasesColorimetric assayImage processingDiagnosis
Escobedo, P., Erenas, M. M., Olmos, A. M., Carvajal, M. A., Chávez, M. T., González, M. A. L., ... & Palma, A. J. (2019). Smartphone-based diagnosis of parasitic infections with colorimetric assays in centrifuge tubes. IEEE Access, 7, 185677-185686.
SponsorshipThis work was supported in part by the Spanish Ministry of Economics and Competitivity under Project CTQ2016-78754-C2-1-R, in part by the European Regional Development Fund (ERDF), and in part by the DestiNA Genomica SL provided reagents, samples and Spin-Tube devices. The work of P. Escobedo was supported by the Spanish Ministry of Education, Culture and Sport (MECD), under Grant (FPU13/05032).
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, xed 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 m 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 de nition 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.