Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks
Abstract Point-of-care testing is essential for individuals with various diseases, as it allows for timely and effective management, preventing complications and promoting overall health. Colorimetric detection is a valuable tool for disease diagnosis due to its simplicity, affordability, and abilit...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06859-9 |
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| author | Mithun Kanchan Pedapudi Anantha Hari Arun Siddhant Rakesh Chutke |
| author_facet | Mithun Kanchan Pedapudi Anantha Hari Arun Siddhant Rakesh Chutke |
| author_sort | Mithun Kanchan |
| collection | DOAJ |
| description | Abstract Point-of-care testing is essential for individuals with various diseases, as it allows for timely and effective management, preventing complications and promoting overall health. Colorimetric detection is a valuable tool for disease diagnosis due to its simplicity, affordability, and ability to provide rapid, qualitative results at the point of care. In this endeavour, we have designed a novel, affordable point-of-care diagnostic device utilizing microfluidic principles, a smartphone camera, and established laboratory colorimetric methods for accurate colorimetry. Our proposed microfluidic device comprises of layers of adhesive poly-vinyl films with micro-wells precision-cut using a cutting printer and stacked on a glass slide. As part of this pilot study, we employed the glucose-oxidase/peroxidase reaction on the microfluidic platform as a demonstration, successfully achieving enzymatic glucose determination. The resulting coloured complex, formed by phenol and 4-aminoantipyrine in the presence of hydrogen peroxide generated during glucose oxidation, is captured at various glucose concentrations using a smartphone camera. Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 97% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms. Furthermore, the classifier exhibits outstanding precision, recall, and F1 score of 96.6%, 96.2%, and 96%, respectively, as validated through our study, showcasing its exceptional predictive capability. The developed CNN model can be successfully used as pre-trained model for future glucose concentration predictions. |
| format | Article |
| id | doaj-art-6b9bb3346f8244e3aed4a0e8db0b253e |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-6b9bb3346f8244e3aed4a0e8db0b253e2025-08-20T04:02:56ZengSpringerDiscover Applied Sciences3004-92612025-07-017811510.1007/s42452-025-06859-9Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networksMithun Kanchan0Pedapudi Anantha Hari Arun1Siddhant Rakesh Chutke2Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Aeronautical and Automobile Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher EducationAbstract Point-of-care testing is essential for individuals with various diseases, as it allows for timely and effective management, preventing complications and promoting overall health. Colorimetric detection is a valuable tool for disease diagnosis due to its simplicity, affordability, and ability to provide rapid, qualitative results at the point of care. In this endeavour, we have designed a novel, affordable point-of-care diagnostic device utilizing microfluidic principles, a smartphone camera, and established laboratory colorimetric methods for accurate colorimetry. Our proposed microfluidic device comprises of layers of adhesive poly-vinyl films with micro-wells precision-cut using a cutting printer and stacked on a glass slide. As part of this pilot study, we employed the glucose-oxidase/peroxidase reaction on the microfluidic platform as a demonstration, successfully achieving enzymatic glucose determination. The resulting coloured complex, formed by phenol and 4-aminoantipyrine in the presence of hydrogen peroxide generated during glucose oxidation, is captured at various glucose concentrations using a smartphone camera. Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 97% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms. Furthermore, the classifier exhibits outstanding precision, recall, and F1 score of 96.6%, 96.2%, and 96%, respectively, as validated through our study, showcasing its exceptional predictive capability. The developed CNN model can be successfully used as pre-trained model for future glucose concentration predictions.https://doi.org/10.1007/s42452-025-06859-9ColorimetryConvolution neural networksDeep learningGlucoseMicrofluidicsSmartphone |
| spellingShingle | Mithun Kanchan Pedapudi Anantha Hari Arun Siddhant Rakesh Chutke Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks Discover Applied Sciences Colorimetry Convolution neural networks Deep learning Glucose Microfluidics Smartphone |
| title | Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks |
| title_full | Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks |
| title_fullStr | Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks |
| title_full_unstemmed | Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks |
| title_short | Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks |
| title_sort | pilot study a low cost point of care microfluidic chip for smartphone integrated colorimetric detection using convolutional neural networks |
| topic | Colorimetry Convolution neural networks Deep learning Glucose Microfluidics Smartphone |
| url | https://doi.org/10.1007/s42452-025-06859-9 |
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