PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Guangyao Li,Yalin Song,Mingyang Liang,Junyang Yu,Rui Zhai
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Approach: This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations (ALFF) and Regional Homogeneity (ReHo) extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification. 
Main results: Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%).
Significance: The proposed method has the potential to become a clinical auxiliary diagnostic tool for Parkinson's disease, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency.&#xD.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"142 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neural engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1741-2552/ad788b","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing Parkinson's disease. Approach: This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations (ALFF) and Regional Homogeneity (ReHo) extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification. Main results: Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%). Significance: The proposed method has the potential to become a clinical auxiliary diagnostic tool for Parkinson's disease, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency. .
PD-ARnet:从静息态 fMRI 诊断帕金森病的深度学习方法。
帕金森病(Parkinson's disease,PD)的临床诊断主要依靠病史、临床症状和体征,主观性强,缺乏敏感性。静息态 fMRI(rs-fMRI)已被证明是诊断帕金森病的有效生物标志物:本研究提出了一种利用rs-fMRI自动诊断帕金森病的深度学习方法,命名为PD-ARnet。具体来说,PD-ARnet 利用从 rs-fMRI 提取的低频波动幅度(ALFF)和区域同质性(ReHo)作为输入。然后通过开发的双分支三维特征提取器对输入进行处理,以执行高级特征提取。在此过程中,将应用相关性驱动加权模块,从两个特征中获取互补信息。随后,开发了注意力增强融合模块,以有效融合两种特征,并将融合后的特征输入全连接层,进行自动诊断分类:结果表明,PD-ARnet 的平均分类准确率为 91.6%(95% 置信区间 [CI]:90.9%,92.4%),精确度为 94.7%(95% 置信区间 [CI]:94.2%,95.1%),召回率为 86.2%(95% 置信区间 [CI]:84.9%,87.4%),F1 分数为 90.2%(95% 置信区间 [CI]:89.3%,91.1%),AUC 为 92.8%(95% 置信区间 [CI]:91.1%,95.0%):该方法有望成为帕金森病的临床辅助诊断工具,减少诊断过程中的主观性,提高诊断效率和一致性。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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