A fast and non‐invasive artificial intelligence olfactory‐like system that aids diagnosis of Parkinson's disease

Author:

Cao Yina1ORCID,Jiang Lina2ORCID,Zhang Jingxin1ORCID,Fu Yanlu1,Li Qiwei1,Fu Wei3ORCID,Zhu Junjiang4ORCID,Xiang Xiaohui1ORCID,Zhao Guohua1,Kong Dongdong5ORCID,Chen Xing3ORCID,Fang Jiajia1ORCID

Affiliation:

1. Department of Neurology The Fourth Affiliated Hospital of Zhejiang University Medical College Zhejiang China

2. Department of Radiology Fourth Affiliated Hospital of Zhejiang University Medical College Zhejiang China

3. Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Ministry of Education of China Zhejiang University Zhejiang China

4. College of Mechanical and Electrical Engineering China Jiliang University Zhejiang China

5. School of Mechatronic Engineering and Automation Shanghai University Shanghai China

Abstract

AbstractBackground and purposeSeveral previous studies have shown that skin sebum analysis can be used to diagnose Parkinson's disease (PD). The aim of this study was to develop a portable artificial intelligence olfactory‐like (AIO) system based on gas chromatographic analysis of the volatile organic compounds (VOCs) in patient sebum and explore its application value in the diagnosis of PD.MethodsThe skin VOCs from 121 PD patients and 129 healthy controls were analyzed using the AIO system and three classic machine learning models were established, including the gradient boosting decision tree (GBDT), random forest and extreme gradient boosting, to assist the diagnosis of PD and predict its severity.ResultsA 20‐s time series of AIO system data were collected from each participant. The VOC peaks at a large number of time points roughly concentrated around 5–12 s were significantly higher in PD subjects. The gradient boosting decision tree model showed the best ability to differentiate PD from healthy controls, yielding a sensitivity of 83.33% and a specificity of 84.00%. However, the system failed to predict PD progression scored by Hoehn−Yahr stage.ConclusionsThis study provides a fast, low‐cost and non‐invasive method to distinguish PD patients from healthy controls. Furthermore, our study also indicates abnormal sebaceous gland secretion in PD patients, providing new evidence for exploring the pathogenesis of PD.

Funder

Medical Science and Technology Project of Zhejiang Province

Publisher

Wiley

Subject

Neurology (clinical),Neurology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3