New deep learning method aids in accurate diagnosis of Parkinsonian diseases

A new deep learning method has been created to aid in the diagnosis of Parkinsonian diseases, according to a study published ahead of print by The Journal of Nuclear Medicine. Using a 3D deep convolution neural network to extract deep metabolic imaging cues from 18Using F-FDG PET, scientists can effectively differentiate Parkinson’s disease from other parkinsonian syndromes, such as multiple system atrophy and progressive supranuclear palsy.

Parkinson’s disease is one of the most common neurodegenerative diseases. According to the Parkinson’s Foundation, more than 10 million people worldwide live with the disease. Accurate diagnosis of Parkinson’s disease is often a challenge, especially in the early stages, because its symptoms overlap considerably with those of other atypical parkinsonian syndromes.

Studies show that 20-30% of patients initially diagnosed with Parkinson’s disease are later found to have multiple system atrophy or progressive supranuclear palsy upon pathological examination. Therefore, the development of accurate indices to differentiate Parkinsonian diseases is of great importance, especially with regard to determining treatment strategies.”

Ping Wu, MD, PhD, Neuroradiologist at PET Center, Huashan Hospital, Fudan University in Shanghai, China

To achieve this goal, the researchers built a 3D deep convolution neural network, known as the Parkinsonism Differential Diagnosis Network (PDD-Net), to automatically identify imaging-related clues that could support the differential diagnosis of parkinsonian diseases. This deep learning method was used to examine the Parkinsonian PET imaging of two groups: more than 2,100 Chinese patients and 90 German patients.

“It is important to note the steps that were taken to improve the reliability of the study,” Wu said. data from Huashan Parkinsonian PET Imaging in Shanghai, China, and performed extensive testing on longitudinal data. Additionally, we studied the German cohort to include external data representing different ethnicities and examination protocols.”

Deep metabolic imaging indices extracted from PDD-Net provided an early and accurate method for the differential diagnosis of parkinsonian syndromes, with high rates of sensitivity and specificity for Parkinson’s disease, multiple system atrophy and supranuclear palsy progressive.

“This work confirms that emerging artificial intelligence can extract detailed information from molecular imaging to improve the differentiation of complex physiology,” Wu said. “Deep learning technology can help physicians maximize the usefulness of imaging in nuclear medicine in the future.”


Society for Nuclear Medicine and Molecular Imaging

Journal reference:

Wu, P. et al. (2022) Differential Diagnosis of Parkinsonism Based on Deep Metabolic Imaging Clues. Journal of Nuclear Medicine.

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