Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dimensionality Reduction
2.2. Linear Dimensionality Reduction
Multidimensional Scaling
2.3. Non-Linear Dimensionality Reduction
2.3.1. Isometric Feature Map**
2.3.2. Locally Linear Embedding (LLE)
2.3.3. Applications of Non-Linear Dimensionality Reduction
2.4. Patients
2.5. Image Dataset
2.6. Multiparametric Brain MRI
2.7. Performance Evaluation
3. Results
4. Discussion
5. Conclusions
6. Patents
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Demographics and Clinical Data on the CombiRx Cohort | ||
---|---|---|
Age (yrs) | 37.7 ± 9.7 | |
Female/Male (ratio) | 72/28 | |
Caucasian | 87.6 | |
Race (%) | African American | 7.2 |
Other | 5.2 | |
Hispanic | 6.3 | |
Ethnicity (%) | Non-Hispanic | 89.5 |
Other | 4.3 | |
Symptom Duration (yrs) | 4.8 ± 5.6 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Uwaeze, J.; Narayana, P.A.; Kamali, A.; Braverman, V.; Jacobs, M.A.; Akhbardeh, A. Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning. Diagnostics 2024, 14, 632. https://doi.org/10.3390/diagnostics14060632
Uwaeze J, Narayana PA, Kamali A, Braverman V, Jacobs MA, Akhbardeh A. Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning. Diagnostics. 2024; 14(6):632. https://doi.org/10.3390/diagnostics14060632
Chicago/Turabian StyleUwaeze, Jason, Ponnada A. Narayana, Arash Kamali, Vladimir Braverman, Michael A. Jacobs, and Alireza Akhbardeh. 2024. "Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning" Diagnostics 14, no. 6: 632. https://doi.org/10.3390/diagnostics14060632