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Assessment of the potential of convolutional neuronal networks in the differential diagnosis of Parkinson’s disease based on brain imaging [123I]FP-CIT SPECT

2022-10Conference object Open access
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dc.contributor.authorValador, N.
dc.contributor.authorOliveira, F. P.
dc.contributor.authorFerreira, P. M.
dc.contributor.authorVieira, Lina Oliveira
dc.contributor.authorCosta, D. C.
dc.date.accessioned2022-12-28T12:22:47Z
dc.date.available2022-12-28T12:22:47Z
dc.date.issued2022-10
dc.description.abstractAim/Introduction: To evaluate the potential of convolutional neural networks (CNN) in the differential diagnosis of Parkinson’s disease (PD) based on [123I]FP-CIT single-photon emission computed tomography (SPECT) images, compared to other machine learning-based classifiers. Materials and Methods: This work included 806 [123I]FP-CIT SPECT brain images (208 health controls and 598 with PD). Data were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For each subject, only the first scan was considered (baseline or screening). The protocol of image acquisition and pre-processing is available at http://www.ppmi-info.org/study-design/research-documentsand-sops/. CNN was compared against k-nearest neighbor (kNN), logistic regression (LG), decision trees (DT), support vector machines (SVM), and artificial neural networks (ANN) classifiers. The CNN classifier was trained with 2-dimensional image patches (dimensions: 88 mm x 82 mm) containing the striatal region, extracted from the head superior-inferior maximum intensity projection. The remaining classifiers were trained with five features extracted from the 3-dimensional striatal region: caudate binding potential, putamen binding potential, putamen to caudate ratio, the volume of the striatal region with “normal uptake”, and the major axis of that region. The minimum values extracted from each cerebral hemisphere were used. The split ratio of the dataset was 75:25 (75% for training and 25% for testing). Each of the five features was also considered individually to assess its potential for classification in terms of performance (accuracy, sensitivity, and specificity). Results: In the test dataset, the accuracy, sensitivity, and specificity of the CNN were 96%, 98%, and 91%, respectively. This finding was very similar to what we obtained with the other classifiers (kNN: 95%, 99%, 85%; LG: 94%, 97%, 86%, DT: 94%, 97%, 84%, SVM: 94%, 98%, 88%, and ANN: 94%, 97%, 86%). The accuracy differences are not statistically significant (Cochran Q test, p = 0.592). Individually, the feature that best differentiates PD from normal scans was the putamen binding potential with 93% accuracy, 93% sensitivity, and 94% specificity in the test dataset, based on the optimal cutoff (1.716) that maximizes Younden’s coefficient in the training dataset. Conclusion: CNN classifier proved to be as robust and accurate as the other classifiers frequently used in the type of problems as in this work, with the great advantage of using images as direct input. All machine learning-based classifiers tested are robust and very accurate in the classification of brain [123I]FP-CIT SPECT scans. Standard visual clinical evaluation should be complemented with quantification classification used also as a training tool.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationValador N, Oliveira FP, Ferreira PM, Vieira L, Costa DC. Assessment of the potential of convolutional neuronal networks in the differential diagnosis of Parkinson’s disease based on brain imaging [123I]FP-CIT SPECT. In: Annual Congress of the European Association of Nuclear Medicine, Barcelona (Spain), October 15-19, 2022. Eur J Nucl Med Mol Imaging. 2022;49 Suppl 1:S243-4.pt_PT
dc.identifier.doi10.1007/s00259-022-05924-4pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/15207
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s00259-022-05924-4pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectNuclear medicinept_PT
dc.subjectParkinson diseasept_PT
dc.subjectBrain imagingpt_PT
dc.subjectMachine learning-classifierpt_PT
dc.titleAssessment of the potential of convolutional neuronal networks in the differential diagnosis of Parkinson’s disease based on brain imaging [123I]FP-CIT SPECTpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPageS244pt_PT
oaire.citation.issueSuppl 1pt_PT
oaire.citation.startPageS243pt_PT
oaire.citation.volume49pt_PT
person.familyNameVieira
person.givenNameLina Oliveira
person.identifier.ciencia-idD316-5085-2DE0
person.identifier.orcid0000-0002-7109-6535
person.identifier.ridP-7426-2015
person.identifier.scopus-author-id55258594400
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication0b528961-126c-42d6-a4e7-2a10423c94b8
relation.isAuthorOfPublication.latestForDiscovery0b528961-126c-42d6-a4e7-2a10423c94b8

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