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Abstract #2959

SHAP Interpretation of Machine Learning Model for Subcortical White Matter Biomarkers in Cognitive Impairment in Multiple Sclerosis Patients

Cristian Montalba1,2,3, Pamela Franco3,4,5, Raul Caulier-Cisterna6, Juan Pablo Cruz7, Claudia Carcamo8,9, and Ethel Ciampi8,10
1Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, 2Radiology Department, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile, 3Millennium Institute for IntelligentHealthcare Engineering - iHEALTH, Pontificia Universidad Catolica de Chile, Santiago, Chile, 4Physics Department, Faculty of Science, Universidad de Santiago, Santiago, Chile, 5School of of Civil Engineering, Computer Science and Telecommunications, Faculty of Engineering, Universidad Finis Terrae, Santiago, Chile, 6Department of Informatics and Computing, Faculty of Engineering,, Universidad Tecnológica Metropolitana, Santiago, Chile, 7Radiology Department, Instituto de Neurocirugía – Dr. Alfonso Asenjo, Santiago, Chile, 8Neurology Department, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile, 9Interdisciplinary Center of Neurosciences, Pontificia Universidad Catolica de Chile, Santiago, Chile, 10Neurology Service, Hospital Dr. Sótero del Río, Santiago, Chile

Synopsis

Keywords: Multiple Sclerosis, Multiple Sclerosis, .

Motivation: Multiple Sclerosis patients present cognitive decline at the early stages of the disease. Current neurocognitive batteries may not identify early changes. FA evaluates microstructural changes in white matter. To consider clinicopathological correlation remains complex and needs to be understood.

Goal(s): A biomarker that could detect patients with cognitive deficits might benefit from early diagnosis and treatment.

Approach: ML to identify subcortical white matter biomarkers between Healthy Controls with Cognitive Preserved and Relapsing-Remitting Multiple Sclerosis patients with or without cognitive impairment in verbal episodic memory.

Results: We found six FA biomarkers, all located in the frontal lobes. These features maximized the accuracy, obtained: 62.22±17.33%.

Impact: Since the MRI is the gold standard for MS diagnosis, we can obtain new insights about not only the patient's condition but also detect early changes in patients with cognitive impairment.

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Keywords