Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs)
Abstract In pulmonary inflammation diseases, like COVID-19, lung involvement and inflammation determine the treatment regime. Respiratory inflammation is typically arisen due to the cytokine storm and the leakage of the vessels for immune cells recruitment. Currently, such a situation is detected by...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-54939-4 |
| _version_ | 1797247326587191296 |
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| author | Mohammadreza Ghaderinia Hamed Abadijoo Ashkan Mahdavian Ebrahim Kousha Reyhaneh Shakibi Seyed Mohammad Reza Taheri Hossein Simaee Ali Khatibi Ali Akbar Moosavi-Movahedi Mohammad Ali Khayamian |
| author_facet | Mohammadreza Ghaderinia Hamed Abadijoo Ashkan Mahdavian Ebrahim Kousha Reyhaneh Shakibi Seyed Mohammad Reza Taheri Hossein Simaee Ali Khatibi Ali Akbar Moosavi-Movahedi Mohammad Ali Khayamian |
| author_sort | Mohammadreza Ghaderinia |
| collection | DOAJ |
| description | Abstract In pulmonary inflammation diseases, like COVID-19, lung involvement and inflammation determine the treatment regime. Respiratory inflammation is typically arisen due to the cytokine storm and the leakage of the vessels for immune cells recruitment. Currently, such a situation is detected by the clinical judgment of a specialist or precisely by a chest CT scan. However, the lack of accessibility to the CT machines in many poor medical centers as well as its expensive service, demands more accessible methods for fast and cheap detection of lung inflammation. Here, we have introduced a novel method for tracing the inflammation and lung involvement in patients with pulmonary inflammation, such as COVID-19, by a simple electrolyte detection in their sputum samples. The presence of the electrolyte in the sputum sample results in the fern-like structures after air-drying. These fern patterns are different in the CT positive and negative cases that are detected by an AI application on a smartphone and using a low-cost and portable mini-microscope. Evaluating 160 patient-derived sputum sample images, this method demonstrated an interesting accuracy of 95%, as confirmed by CT-scan results. This finding suggests that the method has the potential to serve as a promising and reliable approach for recognizing lung inflammatory diseases, such as COVID-19. |
| first_indexed | 2024-04-24T19:56:55Z |
| format | Article |
| id | doaj.art-46e1d2fec9a0437d9cc44b47cb9940d5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T19:56:55Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-46e1d2fec9a0437d9cc44b47cb9940d52024-03-24T12:16:39ZengNature PortfolioScientific Reports2045-23222024-03-0114111110.1038/s41598-024-54939-4Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs)Mohammadreza Ghaderinia0Hamed Abadijoo1Ashkan Mahdavian2Ebrahim Kousha3Reyhaneh Shakibi4Seyed Mohammad Reza Taheri5Hossein Simaee6Ali Khatibi7Ali Akbar Moosavi-Movahedi8Mohammad Ali Khayamian9Institute of Biochemistry and Biophysics, University of TehranInstitute of Biochemistry and Biophysics, University of TehranNano Electronic Center of Excellence, Nano Bio Electronics Devices Lab, School of Electrical and Computer Engineering, University of TehranInstitute of Biochemistry and Biophysics, University of TehranDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares UniversityCondensed Matter National Laboratory, Institute for Research in Fundamental Sciences (IPM)Institute of Biochemistry and Biophysics, University of TehranDepartment of Biotechnology, Faculty of Biological Sciences, Alzahra UniversityInstitute of Biochemistry and Biophysics, University of TehranInstitute of Biochemistry and Biophysics, University of TehranAbstract In pulmonary inflammation diseases, like COVID-19, lung involvement and inflammation determine the treatment regime. Respiratory inflammation is typically arisen due to the cytokine storm and the leakage of the vessels for immune cells recruitment. Currently, such a situation is detected by the clinical judgment of a specialist or precisely by a chest CT scan. However, the lack of accessibility to the CT machines in many poor medical centers as well as its expensive service, demands more accessible methods for fast and cheap detection of lung inflammation. Here, we have introduced a novel method for tracing the inflammation and lung involvement in patients with pulmonary inflammation, such as COVID-19, by a simple electrolyte detection in their sputum samples. The presence of the electrolyte in the sputum sample results in the fern-like structures after air-drying. These fern patterns are different in the CT positive and negative cases that are detected by an AI application on a smartphone and using a low-cost and portable mini-microscope. Evaluating 160 patient-derived sputum sample images, this method demonstrated an interesting accuracy of 95%, as confirmed by CT-scan results. This finding suggests that the method has the potential to serve as a promising and reliable approach for recognizing lung inflammatory diseases, such as COVID-19.https://doi.org/10.1038/s41598-024-54939-4 |
| spellingShingle | Mohammadreza Ghaderinia Hamed Abadijoo Ashkan Mahdavian Ebrahim Kousha Reyhaneh Shakibi Seyed Mohammad Reza Taheri Hossein Simaee Ali Khatibi Ali Akbar Moosavi-Movahedi Mohammad Ali Khayamian Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs) Scientific Reports |
| title | Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs) |
| title_full | Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs) |
| title_fullStr | Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs) |
| title_full_unstemmed | Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs) |
| title_short | Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs) |
| title_sort | smartphone based device for point of care diagnostics of pulmonary inflammation using convolutional neural networks cnns |
| url | https://doi.org/10.1038/s41598-024-54939-4 |
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