Combined with multimodal medical imaging and artificial intelligence for early diagnosis of Parkinson's disease

Topic: Amyloid, tau and blood-test diagnosis

Epidemiological data show that both PD incidence rate and prevalence rate show a significant age-dependent growth trend [3]. Although the average survival time of patients after diagnosis is long and most of them die due to complications, due to the significant difficulties in early diagnosis, most patients have irreversible loss of dopaminergic neurons in substantia nigra when they are diagnosed. Therefore, achieving early and precise diagnosis of Parkinson's disease is of great clinical significance for improving the cure rate of the disease, prolonging the survival period of patients and enhancing their quality of life.The current clinical diagnosis of Parkinson's disease mainly relies on typical symptom recognition, but this diagnosis method based on subjective symptom description of patients has significant limitations. The multimodal fusion of the two not only realizes the correlation analysis between function and structure, but also significantly improves the diagnostic accuracy, making PET-MRI technology an important tool to identify similar neurodegenerative diseases. Sun et al. [9]showed that the constructed multimodal model outperformed the single-modal model regardless of how PET and MRI data were combined and sequenced. The developed PET/MRI radiomics-clinical combined model achieved an area under the receiver operating characteristic curve (AUC) of 0.993, fully demonstrating the significant potential of this technology in the clinical differentiation of Parkinson's disease (PD) and multiple system atrophy (MSA). Another study focused on [ 18F ] FDG PET/MRI [10], by training an artificial intelligence model integrating metabolic, structural and functional information, confirmed that the comprehensive imaging model was significantly superior to the simple clinical diagnosis model in distinguishing PD from MSA, which mutually confirmed the conclusions of Sun et al. [9]. It is worth noting that neither of the first two studies included the gold standard of pathological results. For early Parkinson's patients, the new PET/CT technology has higher resolution on minor changes in small lesions, such as uptake in PD by PET/CT imaging technology, and found that DAT uptake was related to the decrease of glucose metabolism in brain region, while only some visual functions were significantly affected by DAT, and DAT uptake decreased in the order of PP AP and caudate nucleus, and then the average standard uptake ratio ( SUVR ) decreased, resulting in visuo

Key findings

• Epidemiological data show that both PD incidence rate and prevalence rate show a significant age-dependent growth trend [3].
• Although the average survival time of patients after diagnosis is long and most of them die due to complications, due to the significant difficulties in early diagnosis, most patients have irreversible loss of dopaminergic neurons in substantia nigra when they are diagnosed.
• Therefore, achieving early and precise diagnosis of Parkinson's disease is of great clinical significance for improving the cure rate of the disease, prolonging the survival period of patients and enhancing their quality of life.The current clinical diagnosis of Parkinson's disease mainly relies on typical symptom recognition, but this diagnosis method based on subjective symptom description of patients has significant limitations.
• The multimodal fusion of the two not only realizes the correlation analysis between function and structure, but also significantly improves the diagnostic accuracy, making PET-MRI technology an important tool to identify similar neurodegenerative diseases.
• Sun et al. [9]showed that the constructed multimodal model outperformed the single-modal model regardless of how PET and MRI data were combined and sequenced.

Why it matters

This paper discusses combining multimodal medical imaging and artificial intelligence for early diagnosis of Parkinson's disease, reporting high diagnostic accuracy in cited work. It concerns Parkinson's disease diagnosis and does not test any treatment, cure or prevention of Alzheimer's disease.

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Study details

• Study designcross-sectional
• Evidence strengthLow

Read the original

• DOI: 10.3389/fneur.2025.1650968
• PubMed: 41103521
• Original source →
• Free full text: Frontiers in Neurology →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{lu2025-41103521,
  author  = {Lu, Sitong and Gui, Shuang and Wang, Chunyun and Liu, Yu},
  title   = {{Combined with multimodal medical imaging and artificial intelligence for early diagnosis of Parkinson's disease}},
  journal = {Frontiers in Neurology},
  year    = {2025},
  doi     = {10.3389/fneur.2025.1650968},
  pmid    = {41103521},
  url     = {https://doi.org/10.3389/fneur.2025.1650968}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Epidemiology of Parkinson's disease Journal of neural transmission (Vienna, Austria : 1996) · 2017-02-01 · usefulness score 50/100 · Lewy body dementia

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