Functional Medicine

Hudson, Timothy R

doi:10.1016/j.pmr.2020.07.011

Cited by 1 publication

(1 citation statement)

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“…Notably, most studies gather data with patients at rest, omitting motion or function-related data. This omission is significant as rehabilitation medicine, unlike clinical medicine, prioritizes functional impairments (Hudson, 2020). Moreover, the reliance on single-modality data in previous studies may limit a comprehensive understanding of task-state brain functional characteristics in PD patients (Makarious et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Exploring the application and challenges of fNIRS technology in early detection of Parkinson’s disease

Hui,

Jiang,

Wang

et al. 2024

Front. Aging Neurosci.

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BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention.ObjectiveThe goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice.MethodsUsing an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability.ResultsThe SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08.ConclusionThe model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.

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Section: Discussionmentioning

confidence: 99%