PMID- 35944848
OWN - NLM
STAT- MEDLINE
DCOM- 20221216
LR  - 20221221
IS  - 1873-1597 (Electronic)
IS  - 1572-1000 (Linking)
VI  - 40
DP  - 2022 Dec
TI  - Raman spectroscopy combined with machine learning algorithms for rapid detection 
      Primary Sjogren's syndrome associated with interstitial lung disease.
PG  - 103057
LID - S1572-1000(22)00343-X [pii]
LID - 10.1016/j.pdpdt.2022.103057 [doi]
AB  - BACKGROUND: Interstitial lung disease (ILD) is a major complication of Primary 
      Sjogren's syndrome (pSS) patients.It is one of the main factors leading to death. 
      The aim of this study is to evaluate the value of serum Raman spectroscopy 
      combined with machine learning algorithms in the discriminatory diagnosis of 
      patients with Primary Sjogren's syndrome associated with interstitial lung 
      disease (pSS-ILD). METHODS: Raman spectroscopy was performed on the serum of 30 
      patients with pSS, 28 patients with pSS-ILD and 30 healthy controls (HC). First, 
      the data were pre-processed using baseline correction, smoothing, outlier removal 
      and normalization operations. Then principal component analysis (PCA) is used to 
      reduce the dimension of data. Finally, support vector machine(SVM), k nearest 
      neighbor (KNN) and random forest (RF) models are established for classification. 
      RESULTS: In this study, SVM, KNN and RF were used as classification models, where 
      SVM chooses polynomial kernel function (poly). The average accuracy, sensitivity, 
      and precision of the three models were obtained after dimensionality reduction. 
      The Accuracy of SVM (poly) was 5.71% higher than KNN and 6.67% higher than RF; 
      Sensitivity was 5.79% higher than KNN and 8.56% higher than RF; Precision was 
      6.19% higher than KNN and 7.45% higher than RF. It can be seen that the SVM 
      (poly) had better discriminative effect. In summary, SVM (poly) had a fine 
      classification effect, and the average accuracy, sensitivity and precision of 
      this model reached 89.52%, 91.27% and 89.52%, respectively, with an AUC value of 
      0.921. CONCLUSIONS: This study demonstrates that serum RS combined with machine 
      learning algorithms is a valuable tool for diagnosing patients with pSS-ILD. It 
      has promising applications.
CI  - Copyright (c) 2022. Published by Elsevier B.V.
FAU - Wu, Xue
AU  - Wu X
AD  - Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur 
      Autonomous Region, Urumqi 830001, China; Xinjiang Clinical Research Center for 
      Rheumatoid Arthritis, Urumqi 830001, China.
FAU - Chen, Chen
AU  - Chen C
AD  - College of Information Science and Engineering, Xinjiang University, Urumqi 
      830001, China.
FAU - Chen, Xiaomei
AU  - Chen X
AD  - Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur 
      Autonomous Region, Urumqi 830001, China; Xinjiang Clinical Research Center for 
      Rheumatoid Arthritis, Urumqi 830001, China.
FAU - Luo, Cainan
AU  - Luo C
AD  - Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur 
      Autonomous Region, Urumqi 830001, China; Xinjiang Clinical Research Center for 
      Rheumatoid Arthritis, Urumqi 830001, China.
FAU - Lv, Xiaoyi
AU  - Lv X
AD  - College of Software, Xinjiang University, Urumqi 830046,China.
FAU - Shi, Yamei
AU  - Shi Y
AD  - Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur 
      Autonomous Region, Urumqi 830001, China; Xinjiang Clinical Research Center for 
      Rheumatoid Arthritis, Urumqi 830001, China.
FAU - Yang, Jie
AU  - Yang J
AD  - College of Information Science and Engineering, Xinjiang University, Urumqi 
      830001, China.
FAU - Meng, Xinyan
AU  - Meng X
AD  - Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur 
      Autonomous Region, Urumqi 830001, China; Xinjiang Clinical Research Center for 
      Rheumatoid Arthritis, Urumqi 830001, China.
FAU - Chen, Cheng
AU  - Chen C
AD  - College of Software, Xinjiang University, Urumqi 830046,China.
FAU - Su, Jinmei
AU  - Su J
AD  - Department of Rheumatology and Clinical Immunology, Peking Union Medical College 
      Hospital, Beijing, China. Electronic address: annasu2014@163.com.
FAU - Wu, Lijun
AU  - Wu L
AD  - Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur 
      Autonomous Region, Urumqi 830001, China; Xinjiang Clinical Research Center for 
      Rheumatoid Arthritis, Urumqi 830001, China. Electronic address: wwlj330@126.com.
LA  - eng
PT  - Journal Article
DEP - 20220806
PL  - Netherlands
TA  - Photodiagnosis Photodyn Ther
JT  - Photodiagnosis and photodynamic therapy
JID - 101226123
SB  - IM
MH  - Humans
MH  - *Sjogren's Syndrome/complications/diagnosis
MH  - Spectrum Analysis, Raman
MH  - *Photochemotherapy/methods
MH  - *Lung Diseases, Interstitial/complications/diagnosis/drug therapy
MH  - Machine Learning
MH  - Algorithms
MH  - Support Vector Machine
OTO - NOTNLM
OT  - Interstitial lung disease
OT  - Machine learning
OT  - Primary Sjogren's syndrome
OT  - Raman spectroscopy
COIS- Declaration of Competing Interest The authors declare that they have no 
      conflicting financial interests.
EDAT- 2022/08/10 06:00
MHDA- 2022/12/15 06:00
CRDT- 2022/08/09 19:36
PHST- 2022/05/27 00:00 [received]
PHST- 2022/07/15 00:00 [revised]
PHST- 2022/08/05 00:00 [accepted]
PHST- 2022/08/10 06:00 [pubmed]
PHST- 2022/12/15 06:00 [medline]
PHST- 2022/08/09 19:36 [entrez]
AID - S1572-1000(22)00343-X [pii]
AID - 10.1016/j.pdpdt.2022.103057 [doi]
PST - ppublish
SO  - Photodiagnosis Photodyn Ther. 2022 Dec;40:103057. doi: 
      10.1016/j.pdpdt.2022.103057. Epub 2022 Aug 6.