ArticleEuropean journal of nuclear medicine and molecular imaging2022
Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Diagnostic performance of PET-based artificial intelligence for differentiating Parkinson's disease from normal controls or atypical parkinsonism: a systematic review and meta-analysis.Journal of neurology · 2025Pooled it
- The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2023Pooled it
- Advances in biomarkers for Parkinson's disease: from molecular pathology to precision diagnostics.Journal of Zhejiang University. Science. B · 2026Review
- Review
- The Role of DaT-SPECT Imaging in the Evaluation of Progressive Supranuclear Palsy.Life (Basel, Switzerland) · 2026Review
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- The role of neuro-imaging in multiple system atrophy.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Examination of simple artificial intelligence-based analysis of dopamine transporter scintigraphy for supporting a diagnosis of Parkinson's disease.Annals of nuclear medicine · 2026Article
- Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes withEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Annual percentage change of MR Parkinsonism index in progressive supranuclear palsy: a feasibility study.European radiology · 2025Article
- Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson's disease via self-supervised learning with diffusion models.Cell reports. Medicine · 2025Article
- Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.Annals of nuclear medicine · 2025Article
- Baseline [NPJ Parkinson's disease · 2025Article
- Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.European journal of nuclear medicine and molecular imaging · 2025Article
- Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach.European journal of nuclear medicine and molecular imaging · 2025Article
- Development of a radiomics-based model for diagnosis of multiple system atrophy using multimodal MRI.Frontiers in neurology · 2025Article
- Explainable graph neural network based on metabolic brain imaging for differential diagnosis of parkinsonism.Frontiers in aging neuroscience · 2025Article
- Review
- The value of PET/CT in the diagnosis and differential diagnosis of Parkinson's disease: a dual-tracer study.NPJ Parkinson's disease · 2024Article
- Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.Brain sciences · 2024Article
Corrections and comments
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Authors and funding
21 authors at 9 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism.
methodsThis study involved 1017 subjects who underwent DAT PET imaging ([
resultsThe proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP.
conclusionThis study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis.
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