Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm
The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy an...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Photodiagnosis and Photodynamic Therapy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1572100024004629 |
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| author | Fengjiao Yue Si Li Lijuan Wu Xuerong Chen Jianhua Zhu |
| author_facet | Fengjiao Yue Si Li Lijuan Wu Xuerong Chen Jianhua Zhu |
| author_sort | Fengjiao Yue |
| collection | DOAJ |
| description | The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. The fluorescence spectra of blood plasma from 18 healthy volunteers, 12 individuals with latent TB infections and 80 active TB patients are measured and analyzed. By optimizing the ANN structure and activation functions, the ANN three-classification model achieves average classification accuracy of 96.3 %, and the accuracy of healthy persons, latent TB infections and active TB patients are 100 %, 83.3 % and 97.5 %, respectively, which is much better than the results of traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method. To the best of our knowledge, this is the first research work of differentiating latent, active pulmonary TB cases from healthy samples with autofluorescence spectroscopy. As a rapid, accurate, safe, label-free, non-invasive and cost-effective technique, it can be developed as a promising diagnostic tool for the screening of pulmonary TB disease in the early stage. |
| format | Article |
| id | doaj-art-419effdf9d6e40e1b13f8d48928f8149 |
| institution | DOAJ |
| issn | 1572-1000 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photodiagnosis and Photodynamic Therapy |
| spelling | doaj-art-419effdf9d6e40e1b13f8d48928f81492025-08-20T02:49:56ZengElsevierPhotodiagnosis and Photodynamic Therapy1572-10002024-12-015010442610.1016/j.pdpdt.2024.104426Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithmFengjiao Yue0Si Li1Lijuan Wu2Xuerong Chen3Jianhua Zhu4College of Physics, Sichuan University, Chengdu, ChinaDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China; Department of Respiratory Medicine, The Third Hospital of Shenzhen City, Southern University of Science and Technology, Shenzhen, China; Shenzhen Clinical Research Center for Tuberculosis, Shenzhen, China; Corresponding author at: Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.College of Physics, Sichuan University, Chengdu, China; Corresponding author.The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. The fluorescence spectra of blood plasma from 18 healthy volunteers, 12 individuals with latent TB infections and 80 active TB patients are measured and analyzed. By optimizing the ANN structure and activation functions, the ANN three-classification model achieves average classification accuracy of 96.3 %, and the accuracy of healthy persons, latent TB infections and active TB patients are 100 %, 83.3 % and 97.5 %, respectively, which is much better than the results of traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method. To the best of our knowledge, this is the first research work of differentiating latent, active pulmonary TB cases from healthy samples with autofluorescence spectroscopy. As a rapid, accurate, safe, label-free, non-invasive and cost-effective technique, it can be developed as a promising diagnostic tool for the screening of pulmonary TB disease in the early stage.http://www.sciencedirect.com/science/article/pii/S1572100024004629Autofluorescence spectroscopyBlood plasmaPulmonary tuberculosisArtificial neural networkPrincipal component analysisLinear discriminant analysis |
| spellingShingle | Fengjiao Yue Si Li Lijuan Wu Xuerong Chen Jianhua Zhu Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm Photodiagnosis and Photodynamic Therapy Autofluorescence spectroscopy Blood plasma Pulmonary tuberculosis Artificial neural network Principal component analysis Linear discriminant analysis |
| title | Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm |
| title_full | Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm |
| title_fullStr | Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm |
| title_full_unstemmed | Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm |
| title_short | Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm |
| title_sort | rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm |
| topic | Autofluorescence spectroscopy Blood plasma Pulmonary tuberculosis Artificial neural network Principal component analysis Linear discriminant analysis |
| url | http://www.sciencedirect.com/science/article/pii/S1572100024004629 |
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