Deep learning for Parkinson’s disease classification using multimodal and multi-sequences PET/MR images

The proposed DL method shows potential as a high-performance assisting tool for the accurate diagnosis of PD and MSA. A multi-modal and multi-sequence model could further enhance the ability to classify PD.

Authors

Institutions

Publication Details

Journal
EJNMMI Research
Published
2025-05-09
DOI
https://doi.org/10.1186/s13550-025-01245-3
Citations
8
Primary Topic
Neurological disorders and treatments
Type
article
Field-Weighted Citation Impact
11.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deep learning for Parkinson’s disease classification using multimodal and multi-sequences PET/MR images

Tong Chen, Shuwei Sun, Ruimin Wang, Jiajin Liu et al.
8 citations
EJNMMI Research
Neurological disorders and treatments
11.11
article

Deep learning for Parkinson’s disease classification using multimodal and multi-sequences PET/MR images

Tong Chen, Shuwei Sun, Ruimin Wang, Jiajin Liu, Yan Chang
article en
8 citations

Abstract

The proposed DL method shows potential as a high-performance assisting tool for the accurate diagnosis of PD and MSA. A multi-modal and multi-sequence model could further enhance the ability to classify PD.

EJNMMI ResearchVol. 15(1)
Chinese PLA General Hospital (CN), PLA Academy of Military Science (CN)
Openalex Percentile: Top 1%
Neurological disorders and treatments
11.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.