Prediction of Parkinson’s disease by transcranial sonography-based deep learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Psychiatry and Mental health,Neurology (clinical),Dermatology,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s10072-023-07154-4.pdf
Reference27 articles.
1. Yu HX, Guo MR, Li G et al (2020) Association between fatigue and motor progression in Parkinson’s disease in southern Chinese. Neurol Sci 41(1):161–164. https://doi.org/10.1007/s10072-019-04059-z
2. Van Wamelen DJ, Martinez-martin P, Weintraub D et al (2021) The Non-Motor Symptoms Scale in Parkinson’s disease: validation and use. Acta Neurol Scand 143(1):3–12. https://doi.org/10.1111/ane.13336
3. Texier B, Prime M, Atamena D et al (2023) Mortalin/Hspa9 involvement and therapeutic perspective in Parkinson’s disease. Neural Regen Res 18(2):293–298. https://doi.org/10.4103/1673-5374.346487
4. Rodríguez-Pallares J, García-Garrote M, Parga JA et al (2023) Combined cell-based therapy strategies for the treatment of Parkinson’s disease: focus on mesenchymal stromal cells. Neural Regen Res 18(3):478–484. https://doi.org/10.4103/1673-5374.350193
5. Li DH, He YC, Liu J et al (2016) Diagnostic accuracy of transcranial sonography of the substantia nigra in Parkinson’s disease: a systematic review and meta-analysis. Sci Rep 6:20863. https://doi.org/10.1038/srep20863
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1. Attention-enhanced dilated convolution for Parkinson’s disease detection using transcranial sonography;BioMedical Engineering OnLine;2024-07-31
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