Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning
© 2023 Wiley-VCH GmbH..
While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy and machine learning for the rapid and accurate identification of patients with cholecystitis. Significant differences were observed between the fluorescence spectral intensities of the serum of cholecystitis patients (n = 74) serum and those of healthy subjects (n = 71) at 455, 480, 485, 515, 625 and 690 nm. The ratios of characteristic fluorescence spectral peak intensities were first calculated, and principal component analysis (PCA)-linear discriminant analysis (LDA) and PCA-support vector machine (SVM) classification models were then constructed using the ratios as variables. Compared with the PCA-LDA model, the PCA-SVM model displayed better diagnostic performance in differentiating cholecystitis patients from healthy subjects, with an overall accuracy of 96.55%. This exploratory study showed that serum fluorescence spectroscopy combined with the PCA-SVM algorithm has significant potential for the development of a rapid cholecystitis screening method.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
Journal of biophotonics - 16(2023), 8 vom: 15. Aug., Seite e202200354 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dou, Jingrui [VerfasserIn] |
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| Links: |
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| Themen: |
Cholecystitis |
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| Anmerkungen: |
Date Completed 07.08.2023 Date Revised 07.08.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/jbio.202200354 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM35604999X |
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| 500 | |a Date Revised 07.08.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 Wiley-VCH GmbH. | ||
| 520 | |a While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy and machine learning for the rapid and accurate identification of patients with cholecystitis. Significant differences were observed between the fluorescence spectral intensities of the serum of cholecystitis patients (n = 74) serum and those of healthy subjects (n = 71) at 455, 480, 485, 515, 625 and 690 nm. The ratios of characteristic fluorescence spectral peak intensities were first calculated, and principal component analysis (PCA)-linear discriminant analysis (LDA) and PCA-support vector machine (SVM) classification models were then constructed using the ratios as variables. Compared with the PCA-LDA model, the PCA-SVM model displayed better diagnostic performance in differentiating cholecystitis patients from healthy subjects, with an overall accuracy of 96.55%. This exploratory study showed that serum fluorescence spectroscopy combined with the PCA-SVM algorithm has significant potential for the development of a rapid cholecystitis screening method | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a cholecystitis | |
| 650 | 4 | |a diagnosis | |
| 650 | 4 | |a fluorescence spectroscopy | |
| 650 | 4 | |a linear discriminant analysis | |
| 650 | 4 | |a principal component analysis | |
| 650 | 4 | |a serum | |
| 650 | 4 | |a support vector machine | |
| 700 | 1 | |a Dawuti, Wubulitalifu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jintian |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Xiangxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Renyong |e verfasserin |4 aut | |
| 700 | 1 | |a Lü, Guodong |e verfasserin |4 aut | |
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