AI and Neurological Rare & Neuroimmune Diseases ── Parkinson's ── 2026-10-16
Parkinson's disease and parkinsonism: treatment, trials and AI
- Abstract
Background: Multiple system atrophy (MSA) is frequently misdiagnosed as Parkinson's disease (PD) or progressive supranuclear palsy (PSP), posing significant diagnostic challenges.
Objective: The aim of the study was to systematically evaluate the efficacy of machine learning (ML) in differentiating MSA from other neurodegenerative disorders.
Methods: We conducted a systematic search in PubMed, Embase, Cochrane Library, and Web of Science (up to July 5, 2025), including ML‐based MSA diagnostic studies. QUADAS‐2 assessed bias risk, and a bivariate mixed‐effects model synthesized diagnostic metrics.
Results: A total of 51 studies (comprising 3595 MSA patients and 18,189 total cases) were included in the systematic review. Of these, 38 studies contributed to the quantitative synthesis, whereas the remaining 13 studies were summarized qualitatively. In the primary analysis comparing MSA with PD, the pooled sensitivity was 0.86 (95% confidence interval [CI]: 0.81–0.90), specificity 0.87 (95% CI: 0.83–0.90), and the area under the summary receiver operating characteristic (SROC) curve (AUC) 0.93 (95% CI: 0.90–0.95). Overall, pooled sensitivity was 0.85 (95% CI: 0.82–0.88), specificity 0.92 (95% CI: 0.89–0.94), and AUC 0.94 (95% CI: 0.92–0.96). Positron emission tomography (PET)‐based models achieved optimal performance (sensitivity: 0.90, specificity: 0.96, AUC: 0.97), whereas deep learning models outperformed others (sensitivity: 0.93, specificity: 0.98, AUC: 0.99).
Conclusion: ML models integrating PET/magnetic resonance imaging (MRI) data demonstrate high diagnostic accuracy for MSA. Future work should prioritize prospective multicenter validation and standardized external testing to facilitate clinical implementation.
Journal IF-equivalent: 2.0 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Wang G, Nie C, Liu M. Accuracy of Machine Learning in Differential Diagnosis of Multiple System Atrophy: A Systematic Review and Meta‐Analysis. Movement Disord Clin Pract. 2026 Oct 14 [Epub ahead of print]. doi:10.1002/mdc3.70857.Checked: Abstract only - Abstract
Background: Parkinson’s disease (PD) and Progressive Supranuclear Palsy (PSP) are neurodegenerative disorder which have Parkinsonism as core clinical feature. PD and PSP are often misdiagnosed in early phases by conventional magnetic resonance imaging (MRI). Hence the objective of the study is to evaluate accuracy of MRI based machine learning (ML) models for differentiating PD and PSP.
Methods: This is a retrospective study including total of 96 subjects (32 PD, 32 PSP and 32 healthy controls). The basal nuclei region such as substantia nigra (SN), caudate nucleus (CN), putamen, globus pallidus (GP), red nucleus (RN), and subthalamic nucleus (STN) were manually segmented in T2 weighted fluid attenuation inversion recovery (T2W-FLAIR) images and radiomics features (RF) were extracted. The top 20 RF were used to train and test ML models. Performance measures of ML models for differentiating PD, PSP and HC were evaluated.
Results: For differentiating PD from PSP, SN consistently showed highest performance achieving accuracy, F1score and AUC values of 0.95–1.00 across all the tested ML models. RN and STN also demonstrated near perfect classification with several ML models with accuracy and AUC values of 1.00. CN, putamen and GP showed moderate performance (accuracy 38% - 85%). Among classifiers, AdaBoost provided robust performance achieving AUC up to 0.89 and MCC up to 0.72 in test data sets.
Conclusion: MRI based radiomics models from SN, STN and RN provide the most accurate classification of PD and PSP. It can also help in diagnosis of PD and PSP which are undifferentiable in early stages of disease.
Journal IF-equivalent: 1.2 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Khruomo VV, - P, Kadavigere R, Nayak K, Sampathila N, Pendem S, et al. MRI-based machine learning models for differentiating Parkinson’s disease and Progressive Supranuclear Palsy. F1000Res. 2026 Oct 13;15:1709. doi:10.12688/f1000research.190609.1.Checked: Abstract only