Novel deep learning method provides early and

Visualization of average saliency maps of patients with parkinsonian diseases.

image: Visualization of average saliency maps of patients with idiopathic Parkinson’s disease (IPD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) in the training cohort showing characteristic regions contributing to the deep metabolic imaging (DMI) indices. The color corresponds to the importance score indicating the contribution of a region for the generated the deep metabolic imaging (DMI) indices. The color directions (yellow and red vs. cyan and blue) represent different influences on the DMI indices (increased uptake value contributes to the increase or decrease of the probability of IPD, MSA, or PSP in the DMI indices). The arrows pointed to the most salient brain regions including 1: Cerebellum, 2: Midbrain, 3: Putamen, 4: Thalamus.
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Credit: Images created by Ping Wu and Yu Zhao, et al., Huashan Hospital, Fudan University in Shanghai, China and University of Bern in Bern, Switzerland.

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

Parkinson’s disease is one of the most common neurodegenerative disorders. 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 — particularly in the early stages — as its symptoms overlap considerably with those of other atypical parkinsonian syndromes.

“Studies show that 20 to 30 percent of patients with initial diagnoses of Parkinson’s disease were subsequently demonstrated to have multiple system atrophy or progressive supranuclear palsy after pathological examination,” said Ping Wu, MD, PhD, neuroradiologist at PET Center, Huashan Hospital, Fudan University in Shanghai, China. “Therefore, the development of accurate indices to differentiate between parkinsonian diseases is of great importance, specifically with regard to determining treatment strategies.”

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

“It’s important to note the steps that were taken to improve the trustworthiness of the study,” said Wu. “We utilized the largest benchmark dataset of parkinsonian patients with FDG PET from Huashan Parkinsonian PET Imaging database in Shanghai, China, and conducted extensive testing on longitudinal data. In addition, we studied the German cohort to include external data representing different ethnicities and examination protocols. “

The 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 progressive supranuclear palsy.

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

This study was made available online in April 2022.

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The authors of “Differential diagnosis of parkinsonism based on deep metabolic imaging indices”Includes Ping Wu, Yihui Guan, and Chuantao Zuo, PET Center, Huashan Hospital, Fudan University, Shanghai, China, and National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China; Yu Zhao, Department of Nuclear Medicine, University of Bern, Bern, Switzerland, AI Lab, Tencent, Shenzhen, China, and Department of Informatics, Technische Universität, München, Munich, Germany; Jianjun Wu, Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China; Matthias Brendel, Department of Nuclear Medicine, University of Munich, Munich, Germany; Jiaying, Lu, PET Center, Huashan Hospital, Fudan University, Shanghai, China, National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China, and Department of Nuclear Medicine, University of Bern , Bern, Switzerland; Jingjie Ge, Ling Li and Qian Xu, PET Center, Huashan Hospital, Fudan University, Shanghai, China; Alexander Bernhardt, Sabrina Katzdobler and Johannes Levin, Department of Neurology, University of Munich, Munich, Germany; Ian Alberts, Jimin Hong and Axel Rominger, Department of Nuclear Medicine, University of Bern, Bern, Switzerland; Igor Yakushev and Wolfgang Weber, Department of Nuclear Medicine, Technische Universität, München, Munich, Germany; Yimin Sun, Fengtao Liu and Jian Wang, National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China, and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China; Günter U. Höglinger, Department of Neurology, Hannover Medical School, Hannover, Germany; Claudio Bassetti, Department of Neurology, University of Bern, Bern, Switzerland; Wolfgang H. Oertel, Department of Neurology, University of Marburg, Marburg, Germany; and Kuangyu Shi, Department of Nuclear Medicine, University of Bern, Bern, Switzerland and Department of Informatics, Technische Universität München, Munich, Germany.

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