Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

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dc.citation.articleNumber632en_US
dc.citation.issueNumber6en_US
dc.citation.journalTitleDiagnosticsen_US
dc.citation.volumeNumber14en_US
dc.contributor.authorUwaeze, Jasonen_US
dc.contributor.authorNarayana, Ponnada A.en_US
dc.contributor.authorKamali, Arashen_US
dc.contributor.authorBraverman, Vladimiren_US
dc.contributor.authorJacobs, Michael A.en_US
dc.contributor.authorAkhbardeh, Alirezaen_US
dc.date.accessioned2024-07-25T20:55:16Zen_US
dc.date.available2024-07-25T20:55:16Zen_US
dc.date.issued2024en_US
dc.description.abstractBackground: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. Objective: To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. Materials and Methods: Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. Results: LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. Conclusions: NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.en_US
dc.identifier.citationUwaeze, J., Narayana, P. A., Kamali, A., Braverman, V., Jacobs, M. A., & Akhbardeh, A. (2024). Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning. Diagnostics, 14(6), Article 6. https://doi.org/10.3390/diagnostics14060632en_US
dc.identifier.digitaldiagnostics-14-00632-v2en_US
dc.identifier.doihttps://doi.org/10.3390/diagnostics14060632en_US
dc.identifier.urihttps://hdl.handle.net/1911/117510en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleAutomatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learningen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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