PMID- 35155895
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220216
IS  - 2470-1343 (Electronic)
IS  - 2470-1343 (Linking)
VI  - 7
IP  - 5
DP  - 2022 Feb 8
TI  - Artificial Intelligent Olfactory System for the Diagnosis of Parkinson's Disease.
PG  - 4001-4010
LID - 10.1021/acsomega.1c05060 [doi]
AB  - Background: Currently, Parkinson's disease (PD) diagnosis is mainly based on 
      medical history and physical examination, and there is no objective and 
      consistent basis. By the time of diagnosis, the disease would have progressed to 
      the middle and late stages. Pilot studies have shown that a unique smell was 
      present in the skin sebum of PD patients. This increases the possibility of a 
      noninvasive diagnosis of PD using an odor profile. Methods: Fast gas 
      chromatography (GC) combined with a surface acoustic wave sensor with embedded 
      machine learning (ML) algorithms was proposed to establish an artificial 
      intelligent olfactory (AIO) system for the diagnosis of Parkinson's through 
      smell. Sebum samples of 43 PD patients and 44 healthy controls (HCs) from Fourth 
      Affiliated Hospital of Zhejiang University School of Medicine, China, were 
      smelled by the AIO system. Univariate and multivariate methods were used to 
      identify the significant volatile organic compound (VOC) features in the 
      chromatograms. ML algorithms, including support vector machine, random forest 
      (RF), k nearest neighbor (KNN), AdaBoost (AB), and Naive Bayes (NB), were used to 
      distinguish PD patients from HC based on the VOC peaks in the chromatograms of 
      sebum samples. Results: VOC peaks with average retention times of 5.7, 6.0, and 
      10.6 s, respectively, corresponding to octanal, hexyl acetate, and perillic 
      aldehyde, were significantly different in PD and HC. The accuracy of the 
      classification based on the significant features was 70.8%. Based on the odor 
      profile, the classification had the highest accuracy and F1 of the five models 
      with 0.855 from NB and 0.846 from AB, respectively, in the process of model 
      establishing. The highest specificity and sensitivity of the five classifiers 
      were 91.6% from NB and 91.7% from RF and KNN, respectively, in the evaluating 
      set. Conclusions: The proposed AIO system can be used to diagnose PD through the 
      odor profile of sebum. Using the AIO system is helpful for the screening and 
      diagnosis of PD and is conducive to further tracking and frequent monitoring of 
      the PD treatment process.
CI  - (c) 2022 The Authors. Published by American Chemical Society.
FAU - Fu, Wei
AU  - Fu W
AUID- ORCID: 0000-0003-4581-8909
AD  - Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of 
      Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang 310027, 
      China.
FAU - Xu, Linxin
AU  - Xu L
AD  - Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of 
      Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang 310027, 
      China.
FAU - Yu, Qiwen
AU  - Yu Q
AD  - Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of 
      Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang 310027, 
      China.
FAU - Fang, Jiajia
AU  - Fang J
AD  - Department of Neurology, the Fourth Affiliated Hospital, Zhejiang University 
      School of Medicine, Yiwu City, Zhejiang Province 322000, P. R. China.
FAU - Zhao, Guohua
AU  - Zhao G
AD  - Department of Neurology, the Fourth Affiliated Hospital, Zhejiang University 
      School of Medicine, Yiwu City, Zhejiang Province 322000, P. R. China.
FAU - Li, Yi
AU  - Li Y
AD  - Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of 
      Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang 310027, 
      China.
FAU - Pan, Chenying
AU  - Pan C
AD  - Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of 
      Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang 310027, 
      China.
FAU - Dong, Hao
AU  - Dong H
AD  - Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou 311100, China.
FAU - Wang, Di
AU  - Wang D
AUID- ORCID: 0000-0003-1581-4982
AD  - Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou 311100, China.
FAU - Ren, Haiyan
AU  - Ren H
AD  - Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
FAU - Guo, Yi
AU  - Guo Y
AD  - Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
FAU - Liu, Qingjun
AU  - Liu Q
AUID- ORCID: 0000-0002-9442-9355
AD  - Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of 
      Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang 310027, 
      China.
FAU - Liu, Jun
AU  - Liu J
AD  - Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of 
      Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang 310027, 
      China.
FAU - Chen, Xing
AU  - Chen X
AUID- ORCID: 0000-0002-9201-4906
AD  - Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of 
      Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang 310027, 
      China.
LA  - eng
PT  - Journal Article
DEP - 20220126
PL  - United States
TA  - ACS Omega
JT  - ACS omega
JID - 101691658
PMC - PMC8829950
COIS- The authors declare no competing financial interest.
EDAT- 2022/02/15 06:00
MHDA- 2022/02/15 06:01
PMCR- 2022/01/26
CRDT- 2022/02/14 05:36
PHST- 2021/09/13 00:00 [received]
PHST- 2022/01/11 00:00 [accepted]
PHST- 2022/02/14 05:36 [entrez]
PHST- 2022/02/15 06:00 [pubmed]
PHST- 2022/02/15 06:01 [medline]
PHST- 2022/01/26 00:00 [pmc-release]
AID - 10.1021/acsomega.1c05060 [doi]
PST - epublish
SO  - ACS Omega. 2022 Jan 26;7(5):4001-4010. doi: 10.1021/acsomega.1c05060. eCollection 
      2022 Feb 8.