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Classification of Patients with Parkinson’s Disease Using Medical Imaging and Artificial Intelligence Algorithms

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XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 (MEDICON 2019)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 76))

Abstract

The diagnosis of Parkinsonian Syndromes (PS) at early-stages is a challenge. PS usually present similar symptoms, and the diagnosis is mainly clinical, causing often misdiagnosis between PS and other movement disorders. Parkinson’s Disease (PD) is the most common PS, affecting a large part of the worldwide population. Medical imaging such as Magnetic Resonance Imaging (MRI) and Single Photon Emission Computed Tomography (SPECT) are currently being used to detect changes in anatomy and in the dopaminergic system, respectively. SPECT imaging allowed to find a group of patients diagnosed as having PD but without the characteristic decreased uptake of a dopamine analogue, so called “Scans Without Evidence of Dopaminergic Deficit” (SWEDD). Nowadays, deep learning algorithms, such as Convolutional Neural Networks (CNN), are becoming a useful tool in the medical field to detect patterns relevant to diseases in images. This study proposed an approach using CNN for the classification of MRI and SPECT images from PD, SWEDD, and Control subjects, to identify regions-of-interest related to PD. The proposed model achieved an accuracy of 97.4% using MRI images encompassing the mesencephalon and 93.3% with SPECT slices encompassing the basal ganglia. The results suggest that CNN was able to discriminate Control vs. PD and PD vs. SWEDD, CNN achieved accuracies up to 65.7%. Regarding PD vs. SWEDD, this classification obtained an accuracy of 73.3% using MRI images encompassing the mesencephalon and 93.3% with SPECT slices embracing the basal ganglia. The results suggest that CNN was able to discrimination Control vs. PD and PD vs. SWEDD, but not Control vs. SWEDD supporting the fact that SWEDD patients do not show evidence of dopamine deficit. In addition, the classification allowed the identification of the images comprising the mesencephalon or the basal ganglia as the most relevant for the classification.

Research supported by Fundação para a Ciência e Tecnologia (FCT) under the projects UID/BIO/00645/2019, POCI-01-0145-FEDER-016428 and NVIDIA GPU grant program.

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Acknowledgment

The authors would like to thank the financial support from Fundação para a Ciência e Tecnologia (FCT) under the project UID/BIO/00645/2019, Programa Operacional Temático Competitividade e Internacionalização 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: Abbvie, Allergan, Avid, Biogen, BioLegend, Bristol-Myers Squibb, Celgene, Denali, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, Ucb, Verily, Voyager Therapeutics and Golub Capital.

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Authors and Affiliations

  1. Departamento de Engenharia Eletrotécnica da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, 1099-085, Lisbon, Portugal

    Helena R. Pereira

  2. Instituto de Biofísica e Engenharia Biomédica da Faculdade de Ciências da Universidade de Lisboa, 1749-016, Lisbon, Portugal

    Helena R. Pereira & Hugo A. Ferreira

Authors
  1. Helena R. Pereira
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  2. Hugo A. Ferreira
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Correspondence to Helena R. Pereira .

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Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Jorge Henriques

  2. Universidade do Minho, Braga, Portugal

    Nuno Neves

  3. University of Coimbra, Coimbra, Portugal

    Paulo de Carvalho

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Pereira, H.R., Ferreira, H.A. (2020). Classification of Patients with Parkinson’s Disease Using Medical Imaging and Artificial Intelligence Algorithms. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_241

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  • DOI: https://doi.org/10.1007/978-3-030-31635-8_241

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