Open Access Review

Exploring Longitudinal MRI-Based Deep Learning Analysis in Parkinson’s Patients - A Short Survey Focus on Handedness

by Yuan Gu a,* ,  Ziyang Wang b ,  Yuli Wang c ,  Yishu Gong d  and  Chen Li e orcid
a
Department of Statistics, The George Washington University, Washington, USA
b
Department of Computer Science, University of Oxford, Oxford, UK
c
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, USA
d
Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, MA 02138, USA
e
Department of Biology, Chemistry and Pharmacy, Free University of Berlin, 14195, Berlin, Germany
*
Author to whom correspondence should be addressed.
CI  2023, 32; 3(1), 32; https://doi.org/10.58567/ci03010006
Received: 20 November 2023 / Accepted: 13 December 2023 / Published Online: 28 December 2023
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Abstract

Parkinson’s Disease (PD) is a prevalent progressive neurodegenerative condition affecting millions globally. Research has found that individuals with PD have a reduced risk of certain cancers, such as colon, lung, and rectal cancers, but an increased risk of brain cancer. Therefore, there is an urgent need for the development of advanced PD diagnostic methods and for investigating the relationships between risk factors, such as lifestyle due to handedness associated with various types of cancers. Recent ad- vancements in magnetic resonance imaging have enhanced PD diagnosis, reducing misdiagnosis and facilitating more accurate disease progression monitoring. Nevertheless, challenges exist, particularly in the distinction of PD between left-handed and right-handed patients over time. This survey provides an overview of contemporary deep learning-based imag- ing analysis methodologies, encompassing both non-longitudinal and lon- gitudinal contexts. We also explore existing limitations and prospects for refinement to gain deeper insights. These insights are poised to inform the development of personalized treatment strategies for PD patients while elucidating the current disparities between deep learning models and their efficacious implementation in clinical practice.

Keywords: Parkinson’s Disease; PD; deep learning; UNet, Handedness;
Copyright: © 2023 by Gu, Wang, Wang, Gong and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Gu, Y.; Wang, Z.; Wang, Y.; Gong, Y.; Li, C. Exploring Longitudinal MRI-Based Deep Learning Analysis in Parkinson’s Patients - A Short Survey Focus on Handedness. Cancer Insight, 2024, 3, 32. https://doi.org/10.58567/ci03010006
AMA Style
Gu Y, Wang Z, Wang Y, Gong Y, Li C. Exploring Longitudinal MRI-Based Deep Learning Analysis in Parkinson’s Patients - A Short Survey Focus on Handedness. Cancer Insight; 2024, 3(1):32. https://doi.org/10.58567/ci03010006
Chicago/Turabian Style
Gu, Yuan; Wang, Ziyang; Wang, Yuli; Gong, Yishu; Li, Chen 2024. "Exploring Longitudinal MRI-Based Deep Learning Analysis in Parkinson’s Patients - A Short Survey Focus on Handedness" Cancer Insight 3, no.1:32. https://doi.org/10.58567/ci03010006
APA style
Gu, Y., Wang, Z., Wang, Y., Gong, Y., & Li, C. (2024). Exploring Longitudinal MRI-Based Deep Learning Analysis in Parkinson’s Patients - A Short Survey Focus on Handedness. Cancer Insight, 3(1), 32. https://doi.org/10.58567/ci03010006

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