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Challenges in the Diagnosis and Treatment of Parkinson’s Disease

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Neurobiology and Clinical Neuroscience".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2754

Special Issue Editors


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Guest Editor
1. School of Medicine, University of Crete, Heraklion, Greece
2. Neurology Department, University General Hospital of Heraklion, Crete, Greece
3. Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
Interests: Parkinson’s disease; movement disorders; neurogenetics

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Guest Editor
1. School of Medicine, University of Crete, Heraklion, Greece
2. Neurology Department, University General Hospital of Heraklion, Crete, Greece
Interests: Parkinson’s disease; movement disorders; neurogenetics

Special Issue Information

Dear Colleagues,

Parkinson’s disease (PD) is a highly heterogeneous disorder, presenting significant challenges in diagnosis and treatment. While our understanding of the clinical and pathophysiological aspects of PD has deepened, many elements of the disease remain elusive, especially in the early stages when symptoms often overlap with other neurodegenerative conditions. This diagnostic uncertainty frequently leads to delayed treatment, complicating disease management and reducing the potential for optimal patient outcomes. Recent clinical research has focused on refining diagnostic criteria for both PD and prodromal PD. Identifying early clinical, genetic, imaging, laboratory, and digital biomarkers indicative of onset and progression may enhance diagnostic accuracy.

Therapeutically, there is growing interest in strategies that target not only symptom management but also the underlying disease mechanisms, with molecularly informed treatments offering new avenues for intervention. Neuroprotective approaches, including non-pharmaceutical interventions such as exercise, are particularly promising. Equally important are treatment biomarkers that can detect responses to therapy. PD is thought to encompass distinct subtypes, differentiated clinically by motor or non-motor symptom profiles and their pattern of progression. These endophenotypes may be associated with specific genetic substrates or molecular pathways, suggesting underlying differences in disease mechanisms, which could drive personalized treatment approaches. Despite these advancements, significant gaps remain in translating these findings into routine clinical practice. Additionally, although various interventional therapies are available for advanced-stage PD, their management continues to pose challenges for both patients and clinicians.

This Special Issue of Biomedicines highlights clinical research addressing the challenges in diagnosing and treating PD while integrating molecular perspectives where relevant. Key areas of interest include the detection of PD subtypes and the identification of diagnostic, progression, and treatment biomarkers, along with their incorporation into clinical workflows. We also focus on the development of early-stage intervention strategies and diagnostic tools, as well as treatment approaches for both early and advanced-stage PD, including the management of drug-induced complications and updates on novel targeted treatments or interventional therapies.

Our goal is to inspire further research and clinical innovation, ultimately enhancing diagnostic precision, improving treatment outcomes, and supporting the development of personalized approaches to patient care.

Dr. Iro Boura
Prof. Dr. Cleanthe Spanaki
Guest Editors

Manuscript Submission Information

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Keywords

  • Parkinson’s disease
  • diagnosis
  • treatment
  • pathophysiology
  • biomarkers
  • prodromal
  • advanced therapies
  • subtypes
  • imaging
  • genetic

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Published Papers (2 papers)

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Research

20 pages, 512 KiB  
Article
Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson’s Disease from Other Forms of Parkinsonism
by Rana M. Khalil, Lisa M. Shulman, Ann L. Gruber-Baldini, Stephen G. Reich, Joseph M. Savitt, Jeffrey M. Hausdorff, Rainer von Coelln and Michael P. Cummings
Biomedicines 2025, 13(3), 572; https://doi.org/10.3390/biomedicines13030572 - 25 Feb 2025
Viewed by 287
Abstract
Background/Objectives: Parkinson’s Disease (PD) and other forms of parkinsonism share motor symptoms, including tremor, bradykinesia, and rigidity. The overlap in their clinical presentation creates a diagnostic challenge, as conventional methods rely heavily on clinical expertise, which can be subjective and inconsistent. This highlights [...] Read more.
Background/Objectives: Parkinson’s Disease (PD) and other forms of parkinsonism share motor symptoms, including tremor, bradykinesia, and rigidity. The overlap in their clinical presentation creates a diagnostic challenge, as conventional methods rely heavily on clinical expertise, which can be subjective and inconsistent. This highlights the need for objective, data-driven approaches such as machine learning (ML) in this area. However, applying ML to clinical datasets faces challenges such as imbalanced class distributions, small sample sizes for non-PD parkinsonism, and heterogeneity within the non-PD group. Methods: This study analyzed wearable sensor data from 260 PD participants and 18 individuals with etiologically diverse forms of non-PD parkinsonism, which were collected during clinical mobility tasks using a single sensor placed on the lower back. We evaluated the performance of ML models in distinguishing these two groups and identified the most informative mobility tasks for classification. Additionally, we examined the clinical characteristics of misclassified participants and presented case studies of common challenges in clinical practice, including diagnostic uncertainty at the patient’s initial visit and changes in diagnosis over time. We also suggested potential steps to address the dataset challenges which limited the models’ performance. Results: Feature importance analysis revealed the Timed Up and Go (TUG) task as the most informative for classification. When using the TUG test alone, the models’ performance exceeded that of combining all tasks, achieving a balanced accuracy of 78.2%, which is within 0.2% of the balanced diagnostic accuracy of movement disorder experts. We also identified differences in some clinical scores between the participants correctly and falsely classified by our models. Conclusions: These findings demonstrate the feasibility of using ML and wearable sensors for differentiating PD from other parkinsonian disorders, addressing key challenges in its diagnosis and streamlining diagnostic workflows. Full article
(This article belongs to the Special Issue Challenges in the Diagnosis and Treatment of Parkinson’s Disease)
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Figure 1

Figure 1
<p>Study workflow. Pksm: non-PD parkinsonism; AUC-ROC: area under the receiver operating characteristic curve.</p>
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<p>Group feature importance for the classification of Parkinson’s disease and non-PD parkinsonism. The ranking of groups is determined based on their importance, which is computed by simultaneously permuting features within each group and measuring the average decrease in accuracy between the original and permuted data. Under the null hypothesis, which posits no association between the group of predictor variables and the model’s prediction, permutation should result in no impact on predictive performance.</p>
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14 pages, 958 KiB  
Article
Advances in the Neuro-Rehabilitation of Parkinson’s Disease: Insights from a Personalized Multidisciplinary Innovative Pathway
by Maria Grazia Maggio, Mirjam Bonanno, Alfredo Manuli, Rosaria De Luca, Giuseppe Di Lorenzo, Angelo Quartarone and Rocco Salvatore Calabrò
Biomedicines 2024, 12(11), 2426; https://doi.org/10.3390/biomedicines12112426 - 23 Oct 2024
Viewed by 1891
Abstract
Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that requires comprehensive and personalized rehabilitation. This retrospective study focused primarily on the usability and patient acceptability of the innovative pathway. In addition, the secondary objective was to evaluate the effectiveness of a [...] Read more.
Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that requires comprehensive and personalized rehabilitation. This retrospective study focused primarily on the usability and patient acceptability of the innovative pathway. In addition, the secondary objective was to evaluate the effectiveness of a personalized and multidisciplinary rehabilitation pathway on cognitive function, especially executive functions. Methods: We conducted a retrospective study on 80 patients with PD (Hoehn and Yahr scores 1–3). Patients were divided into an experimental group (EG), which received the innovative pathway, and a control group (CG), which received traditional therapy. The rehabilitation program included three phases: initial outpatient assessment, a two-month inpatient program, and a telerehabilitation phase in a day hospital (DH) or home environment. Interventions combined traditional therapies with treatments based on robotic and virtual reality. Cognitive assessments (Mini Mental State Examination—MMSE—and frontal assessment battery—FAB), mood (Hamilton Rating Scale—Depression—HRS-D), anxiety (HRS-Anxiety—HRS-A), and goals achievement (GAS) were the primary outcome measures. Results: At baseline, there were no significant differences between the groups in terms of age, gender, education, or test scores. After rehabilitation, EG showed significant improvements in all measures (p < 0.001), particularly in cognitive tests and goal achievement. CG improved in GAS (p < 0.001) and mood (HRS-D, p = 0.0012), but less than EG. No significant changes were observed in the MMSE of CG (p = 0.23) or FAB (p = 0.003). Conclusions: This study highlights the high usability and acceptability of VR and robotics in PD rehabilitation, contributing to improved adherence and patient engagement. The experimental group showed greater cognitive benefits, particularly in executive functions. These results are in line with the existing literature on personalized technology-based rehabilitation strategies for PD. Full article
(This article belongs to the Special Issue Challenges in the Diagnosis and Treatment of Parkinson’s Disease)
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Figure 1

Figure 1
<p>Flowchart of patient selection process.</p>
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<p>Innovative rehabilitation pathway for patients affected by PD. Legend: A = C-Mill treadmill; B = Lokomat; C = the Rysen system; D = BTS Nirvana room; E = Virtual Reality Rehabilitation System (VRRS)—Evo.</p>
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