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Título: | Automated low-cost smartphone-based lateral flow saliva test reader for drugs-of-abuse detection |
Autor: | Carrio, Adrián; Sampedro, Carlos; Sánchez-López, José L.; Pimienta, Miguel; Campoy, Pascual CSIC ORCID | Palabras clave: | Drugs-of-abuse Smartphone Computer vision Machine learning Neural networks Diagnostics |
Fecha de publicación: | 2015 | Editor: | Molecular Diversity Preservation International | Citación: | Sensors 15: 29569- 29593 (2015) | Resumen: | Lateral flow assay tests are nowadays becoming powerful, low-cost diagnostic tools. Obtaining a result is usually subject to visual interpretation of colored areas on the test by a human operator, introducing subjectivity and the possibility of errors in the extraction of the results. While automated test readers providing a result-consistent solution are widely available, they usually lack portability. In this paper, we present a smartphone-based automated reader for drug-of-abuse lateral flow assay tests, consisting of an inexpensive light box and a smartphone device. Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results. A deep validation of the system has been carried out showing the high accuracy of the system. The proposed approach, applicable to any line-based or color-based lateral flow test in the market, effectively reduces the manufacturing costs of the reader and makes it portable and massively available while providing accurate, reliable results. | URI: | http://hdl.handle.net/10261/131616 | DOI: | 10.3390/s151129569 | Identificadores: | doi: 10.3390/s151129569 issn: 1424-8220 |
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