Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms
Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens. To unravel the relationship between periodontitis and systemic diseases, it is very important to correctly discriminate major periodontal pathogens. To realize convenient, efficient, and high-accura...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
World Scientific Publishing
2022-05-01
|
| Series: | Journal of Innovative Optical Health Sciences |
| Subjects: | |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S1793545822400016 |
| _version_ | 1818554221565313024 |
|---|---|
| author | Juan Zhang Yiping Liu Hongxiao Li Shisheng Cao Xin Li Huijuan Yin Ying Li Xiaoxi Dong Xu Zhang |
| author_facet | Juan Zhang Yiping Liu Hongxiao Li Shisheng Cao Xin Li Huijuan Yin Ying Li Xiaoxi Dong Xu Zhang |
| author_sort | Juan Zhang |
| collection | DOAJ |
| description | Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens. To unravel the relationship between periodontitis and systemic diseases, it is very important to correctly discriminate major periodontal pathogens. To realize convenient, efficient, and high-accuracy bacterial species classification, the authors use Raman spectroscopy combined with machine learning algorithms to distinguish three major periodontal pathogens Porphyromonas gingivalis (Pg), Fusobacterium nucleatum (Fn), and Aggregatibacter actinomycetemcomitans (Aa). The result shows that this novel method can successfully discriminate the three above-mentioned periodontal pathogens. Moreover, the classification accuracies for the three categories of the original data were 94.7% at the sample level and 93.9% at the spectrum level by the machine learning algorithm extra trees. This study provides a fast, simple, and accurate method which is very beneficial to differentiate periodontal pathogens. |
| first_indexed | 2024-12-12T09:36:31Z |
| format | Article |
| id | doaj.art-facd4c80bd7845148b2a16c0d460eaf5 |
| institution | Directory Open Access Journal |
| issn | 1793-5458 1793-7205 |
| language | English |
| last_indexed | 2024-12-12T09:36:31Z |
| publishDate | 2022-05-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj.art-facd4c80bd7845148b2a16c0d460eaf52022-12-22T00:28:43ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052022-05-01150310.1142/S1793545822400016Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithmsJuan Zhang0Yiping Liu1Hongxiao Li2Shisheng Cao3Xin Li4Huijuan Yin5Ying Li6Xiaoxi Dong7Xu Zhang8School of Dentistry, Tianjin Medical University, 12 Qixiangtai Road, Heping District, Tianjin 300070, P. R. ChinaSchool of Dentistry, Tianjin Medical University, 12 Qixiangtai Road, Heping District, Tianjin 300070, P. R. ChinaChinese Academy of Medical Science & Peking Union Medical College, Institute of Biomedical Engineering, 236 Baidi Road, Tianjin 300192, P. R. ChinaSchool of Dentistry, Tianjin Medical University, 12 Qixiangtai Road, Heping District, Tianjin 300070, P. R. ChinaSchool of Dentistry, Tianjin Medical University, 12 Qixiangtai Road, Heping District, Tianjin 300070, P. R. ChinaChinese Academy of Medical Science & Peking Union Medical College, Institute of Biomedical Engineering, 236 Baidi Road, Tianjin 300192, P. R. ChinaSchool of Dentistry, Tianjin Medical University, 12 Qixiangtai Road, Heping District, Tianjin 300070, P. R. ChinaChinese Academy of Medical Science & Peking Union Medical College, Institute of Biomedical Engineering, 236 Baidi Road, Tianjin 300192, P. R. ChinaSchool of Dentistry, Tianjin Medical University, 12 Qixiangtai Road, Heping District, Tianjin 300070, P. R. ChinaPeriodontitis is closely related to many systemic diseases linked by different periodontal pathogens. To unravel the relationship between periodontitis and systemic diseases, it is very important to correctly discriminate major periodontal pathogens. To realize convenient, efficient, and high-accuracy bacterial species classification, the authors use Raman spectroscopy combined with machine learning algorithms to distinguish three major periodontal pathogens Porphyromonas gingivalis (Pg), Fusobacterium nucleatum (Fn), and Aggregatibacter actinomycetemcomitans (Aa). The result shows that this novel method can successfully discriminate the three above-mentioned periodontal pathogens. Moreover, the classification accuracies for the three categories of the original data were 94.7% at the sample level and 93.9% at the spectrum level by the machine learning algorithm extra trees. This study provides a fast, simple, and accurate method which is very beneficial to differentiate periodontal pathogens.https://www.worldscientific.com/doi/10.1142/S1793545822400016Raman spectroscopyperiodontal pathogenmachine learning algorithmdiscrimination |
| spellingShingle | Juan Zhang Yiping Liu Hongxiao Li Shisheng Cao Xin Li Huijuan Yin Ying Li Xiaoxi Dong Xu Zhang Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms Journal of Innovative Optical Health Sciences Raman spectroscopy periodontal pathogen machine learning algorithm discrimination |
| title | Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms |
| title_full | Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms |
| title_fullStr | Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms |
| title_full_unstemmed | Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms |
| title_short | Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms |
| title_sort | discrimination of periodontal pathogens using raman spectroscopy combined with machine learning algorithms |
| topic | Raman spectroscopy periodontal pathogen machine learning algorithm discrimination |
| url | https://www.worldscientific.com/doi/10.1142/S1793545822400016 |
| work_keys_str_mv | AT juanzhang discriminationofperiodontalpathogensusingramanspectroscopycombinedwithmachinelearningalgorithms AT yipingliu discriminationofperiodontalpathogensusingramanspectroscopycombinedwithmachinelearningalgorithms AT hongxiaoli discriminationofperiodontalpathogensusingramanspectroscopycombinedwithmachinelearningalgorithms AT shishengcao discriminationofperiodontalpathogensusingramanspectroscopycombinedwithmachinelearningalgorithms AT xinli discriminationofperiodontalpathogensusingramanspectroscopycombinedwithmachinelearningalgorithms AT huijuanyin discriminationofperiodontalpathogensusingramanspectroscopycombinedwithmachinelearningalgorithms AT yingli discriminationofperiodontalpathogensusingramanspectroscopycombinedwithmachinelearningalgorithms AT xiaoxidong discriminationofperiodontalpathogensusingramanspectroscopycombinedwithmachinelearningalgorithms AT xuzhang discriminationofperiodontalpathogensusingramanspectroscopycombinedwithmachinelearningalgorithms |