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Updated: Nov 9, 2025

Semi-Quantitative Determination of Dopaminergic Neuron Density in the Substantia Nigra of Rodent Models using Automated Image Analysis
Published on: February 2, 2021
Neuropathological correlation supports automated image-based differential diagnosis in parkinsonism
Katharina A Schindlbeck1, Deepak K Gupta2,3, Chris C Tang1
1Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
Purpose:
Up to 25% of patients diagnosed as idiopathic Parkinson's disease (IPD) have an atypical parkinsonian syndrome (APS). We had previously validated an automated image-based algorithm to discriminate between IPD, multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). While the algorithm was accurate with respect to the final clinical diagnosis after long-term expert follow-up, its relationship to the initial referral diagnosis and to the neuropathological gold standard is not known.
Methods:
Patients with an uncertain diagnosis of parkinsonism were referred for 18F-fluorodeoxyglucose (FDG) PET to classify patients as IPD or as APS based on the automated algorithm. Patients were followed by a movement disorder specialist and subsequently underwent neuropathological examination. The image-based classification was compared to the neuropathological diagnosis in 15 patients with parkinsonism.
Results:
At the time of referral to PET, the clinical impression was only 66.7% accurate. The algorithm correctly identified 80% of the cases as IPD or APS (p = 0.02) and 87.5% of the APS cases as MSA or PSP (p = 0.03). The final clinical diagnosis was 93.3% accurate (p < 0.001), but needed several years of expert follow-up.
Conclusion:
The image-based classifications agreed well with autopsy and can help to improve diagnostic accuracy during the period of clinical uncertainty.
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