Diagnosis of multiple sclerosis subtype through machine learning analysis of frontal cortex metabolite profiles
(2019) 2019.- Abstract
- The onset and progression of multiple sclerosis (MS) is accompanied by changes in brain biochemistry. Magnetic resonance spectroscopy (MRS) is a powerful tool for investigating these changes in vivo. Machine learning analysis of MRS-derived biochemical profiles may reveal metabolic patterns inherent in certain MS subtypes to inform their diagnosis. By employing a feature set of only metabolite concentrations derived from brain MRS data acquired at 7 Tesla, we achieved an 80% validation set accuracy for differentiating MS patients from healthy controls and a 70% validation set accuracy for differentiating relapsing-remitting and progressive MS patients.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/321b3fc6-cf02-4220-bdbc-b48e2f94dcd3
- author
- Kurada, Abhinav V ; Swanberg, Kelley M. LU ; Prinsen, Hetty and Juchem, Christoph
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- host publication
- Proc Int Soc Magn Reson Med
- volume
- 2019
- language
- English
- LU publication?
- no
- id
- 321b3fc6-cf02-4220-bdbc-b48e2f94dcd3
- date added to LUP
- 2023-09-18 15:51:06
- date last changed
- 2024-08-07 09:45:04
@inproceedings{321b3fc6-cf02-4220-bdbc-b48e2f94dcd3, abstract = {{The onset and progression of multiple sclerosis (MS) is accompanied by changes in brain biochemistry. Magnetic resonance spectroscopy (MRS) is a powerful tool for investigating these changes in vivo. Machine learning analysis of MRS-derived biochemical profiles may reveal metabolic patterns inherent in certain MS subtypes to inform their diagnosis. By employing a feature set of only metabolite concentrations derived from brain MRS data acquired at 7 Tesla, we achieved an 80% validation set accuracy for differentiating MS patients from healthy controls and a 70% validation set accuracy for differentiating relapsing-remitting and progressive MS patients.}}, author = {{Kurada, Abhinav V and Swanberg, Kelley M. and Prinsen, Hetty and Juchem, Christoph}}, booktitle = {{Proc Int Soc Magn Reson Med}}, language = {{eng}}, title = {{Diagnosis of multiple sclerosis subtype through machine learning analysis of frontal cortex metabolite profiles}}, volume = {{2019}}, year = {{2019}}, }