TY - GEN
T1 - Combination of Medical Imaging and Demographic Data for Parkinson’s Disease Diagnosis
AU - Pereira, Helena Rico
AU - Fonseca, José Manuel
AU - Ferreira, Hugo Alexandre
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FBIO%2F00645%2F2019/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT#
Programa Operacional Tem?tico Competitividade e Internacionaliza??o under the project POCI-01-0145-FEDER-016428, to the NVIDIA GPU Grant Program and to work by PPMI personnel that went into accumulating the data, as well as funding of the study. PPMI ? a public-private partnership ? is funded by the Michael J. Fox Foundation for Parkinson?s Research and industry partners: https://www.ppmi-info.org/about-ppmi/who-we-are/study-sponsors/.
PY - 2020
Y1 - 2020
N2 - The identification of biomarkers to discriminate Parkinson’s Disease from other motor diseases is crucial to provide suitable treatment to patients. This study proposes a novel approach for the classification of structural Magnetic Resonance Imaging (MRI), Dopamine Transporter scan data (DaTscan) and demographic information (age and gender) to differentiate PD patients, “Scans Without Evidence for Dopaminergic Deficit” (SWEDD) patients and healthy control subjects using Convolutional Neural Networks (CNN). In Control vs PD, the accuracy of the classifier increased by adding subject gender from 94.5% to 96.0%, while in PD vs SWEDD adding age lead to 88.7% accuracy using slices encompassing the basal ganglia. The CNN was not able to successfully discriminate SWEDD vs Control. Our results suggested that pattern changes in slices encompassing the basal ganglia and the mesencephalon are relevant biomarkers for PD suggesting that this approach may have the potential to aid in PD biomarkers detection.
AB - The identification of biomarkers to discriminate Parkinson’s Disease from other motor diseases is crucial to provide suitable treatment to patients. This study proposes a novel approach for the classification of structural Magnetic Resonance Imaging (MRI), Dopamine Transporter scan data (DaTscan) and demographic information (age and gender) to differentiate PD patients, “Scans Without Evidence for Dopaminergic Deficit” (SWEDD) patients and healthy control subjects using Convolutional Neural Networks (CNN). In Control vs PD, the accuracy of the classifier increased by adding subject gender from 94.5% to 96.0%, while in PD vs SWEDD adding age lead to 88.7% accuracy using slices encompassing the basal ganglia. The CNN was not able to successfully discriminate SWEDD vs Control. Our results suggested that pattern changes in slices encompassing the basal ganglia and the mesencephalon are relevant biomarkers for PD suggesting that this approach may have the potential to aid in PD biomarkers detection.
KW - Convolutional Neural Networks
KW - DaTscan SPECT
KW - MRI
KW - Parkinson’s Disease
KW - SWEDD
UR - http://www.scopus.com/inward/record.url?scp=85084812513&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-45124-0_32
DO - 10.1007/978-3-030-45124-0_32
M3 - Conference contribution
AN - SCOPUS:85084812513
SN - 978-3-030-45123-3
T3 - IFIP Advances in Information and Communication Technology
SP - 339
EP - 346
BT - Technological Innovation for Life Improvement - 11th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Farhadi, Nastaran
A2 - Lopes, Fábio
A2 - Pereira, Helena
PB - Springer
CY - Cham
T2 - 11th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020
Y2 - 1 July 2020 through 3 July 2020
ER -