An improved approach for early diagnosis of Parkinson’s disease using advanced DL models and image alignment

Kanagaraj, S.; Hema, M.S.; Guptha, M. Nageswara

doi:10.1080/00051144.2023.2284030

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Timely detection and prompt intervention play a critical role in minimizing the consequences of PD and maximizing the effectiveness of treatment. The ability to identify the subtle indications and manifestations of PD at its early stages enables the implementation of suitable therapeutic measures with the goal of mitigating symptoms, maintaining motor abilities, and enhancing the overall quality of life (Kanagaraj et al, 2024 ; Xia et al, 2024 ).…”

Section: Introductionmentioning

confidence: 99%

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection

Kim,

Krichen,

Ojo

et al. 2024

Front. Comput. Neurosci.

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Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification. This study encompasses a comprehensive evaluation aimed at assessing the accuracy of various predictive models, particularly deep learning methods when combined with SS-DRPL. Two deep learning architectures, namely hybrid Long Short-Term Memory and Recurrent Neural Networks (LSTM-RNN) and Deep Neural Networks (DNN), are employed and compared in terms of their ability to detect voice-based PD cases accurately. Additionally, several traditional machine learning models are also included to establish a baseline for comparison. The findings of the study reveal that the incorporation of SS-DRPL leads to improved model performance across all experimental setups. Notably, the LSTM-RNN architecture augmented with SS-DRPL achieves the highest F1-score of 0.94, indicating its superior ability to detect PD cases using voice-based data effectively. This outcome underscores the efficacy of SS-DRPL in enabling deep learning models to learn intricate patterns and correlations within the data, thereby facilitating more accurate PD classification.

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Section: Introductionmentioning

confidence: 99%

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection

Kim,

Krichen,

Ojo

et al. 2024

Front. Comput. Neurosci.

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Gender variability in machine learning based subcortical neuroimaging for Parkinson’s disease diagnosis

Islam,

Khanam

2024

ACI

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PurposeThis study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.Design/methodology/approachMultiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.FindingsThe study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.Originality/valueThis study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

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An improved approach for early diagnosis of Parkinson’s disease using advanced DL models and image alignment

Cited by 2 publications

References 22 publications

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection

Gender variability in machine learning based subcortical neuroimaging for Parkinson’s disease diagnosis

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