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Automatic Detection of Nigrosome Degeneration in Susceptibility-Weighted MRI for Computer-Aided Diagnosis of Parkinson’s Disease Using Machine Learning
GARCIA SANTA CRUZ, Beatriz; HUSCH, Andreas; HERTEL, Frank
2020In Movement Disorders
Peer reviewed
 

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Mots-clés :
Parkinson Disease; Neuroimagine; Computer-Aid-Diagnosis; swallow-tail
Résumé :
[en] Objective: Automatize the detection of ‘swallow-tail’ appearance in substantia nigra dopaminergic neurons using MRI for more robust tests on Parkinson’s disease (PD) diagnosis. Background: Differential diagnosis of PD is challenging even in specialized centers. The use of imaging techniques can be bene cial for the diagnosis. Although DaTSCAN has been proven to be clinically useful, it is not widely available and has radiation risk and high-cost associated. Therefore, MRI scans for PD diagnosis offer several advantages over DaTSCAN [1]. Recent literature shows strong evidence of high diagnostic accuracy using the ‘swallow-tail’ shape of the dorsolateral substantia nigra in 3T – SWI [2]. Nevertheless, the majority of such studies rely on the subjective opinion of experts and manual methods for the analysis to assess the accuracy of these features. Alternatively, we propose a fully automated solution to evaluate the absence or presence of this feature for computer-aided diagnosis (CAD) of PD. Method: Restrospective study of 27 PD and 18 non-PD was conducted, including standard high-resolution 3D MRI – T1 & SWI sequences (additionally, T2 scans were used to increase the registration references). Firstly, spatial registration and normalization of the images were performed. Then, the ROI was extracted using atlas references. Finally, a supervised machine learning model was built using 5-fold-within-5-fold nested cross-validation. Results: Preliminary results show signi cant sensitivity (0.92) and ROC AUC (0.82), allowing for automated classi cation of patients based on swallow-tail biomarker from MRI. Conclusion: Detection of nigrosome degeneration (swallow-tail biomarker) in accessible brain imaging techniques can be automatized with signi cant accuracy, allowing for computer-aided PD diagnosis. References: [1] Schwarz, S. T., Xing, Y., Naidu, S., Birchall, J., Skelly, R., Perkins, A., ... & Gowland, P. (2017). Protocol of a single group prospective observational study on the diagnostic value of 3T susceptibility weighted MRI of nigrosome-1 in patients with parkinsonian symptoms: the N3iPD study (nigrosomal iron imaging in Parkinson’s disease). BMJ open, 7(12), e016904. [2] – Schwarz, S. T., Afzal, M., Morgan, P. S., Bajaj, N., Gowland, P. A., & Auer, D. P. (2014). The ‘swallow tail’ appearance of the healthy nigrosome –a new accurate test of Parkinson’s disease: a case-control and retrospective cross-sectional MRI study at 3T. PloS one, 9(4).
Disciplines :
Sciences de la santé humaine: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
GARCIA SANTA CRUZ, Beatriz ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
HUSCH, Andreas  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
HERTEL, Frank ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Automatic Detection of Nigrosome Degeneration in Susceptibility-Weighted MRI for Computer-Aided Diagnosis of Parkinson’s Disease Using Machine Learning
Date de publication/diffusion :
12 septembre 2020
Nom de la manifestation :
International Parkinson and movement disorders society congress 2020
Organisateur de la manifestation :
International Parkinson and movement disorders society
Date de la manifestation :
from 12-09-2020 to 16-09-2020
Manifestation à portée :
International
Titre du périodique :
Movement Disorders
ISSN :
0885-3185
eISSN :
0885-3185
Peer reviewed :
Peer reviewed
Focus Area :
Systems Biomedicine
Computational Sciences
Commentaire :
Published abstract (Ref: 577)
Disponible sur ORBilu :
depuis le 31 octobre 2020

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