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Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI
Diagnostic and Interventional Imaging
|July 12, 2020
Summary
This study developed a machine learning algorithm to predict multiple sclerosis disability scores using MRI scans and patient data. The model accurately forecasts long-term Expanded Disability Status Scale (EDSS) progression.
Area of Science:
- Neuroimaging
- Machine Learning in Medicine
- Neurology
Background:
- Multiple sclerosis (MS) is a chronic neurological disease characterized by unpredictable disability progression.
- Accurate prediction of disability is crucial for managing MS and tailoring patient treatment.
- Current prediction methods often lack precision or require extensive clinical data.
Purpose of the Study:
- To develop a novel algorithm for predicting the Expanded Disability Status Scale (EDSS) score in multiple sclerosis patients.
- To utilize only age, sex, and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) data for prediction.
- To combine deep learning and classical machine learning approaches for enhanced predictive accuracy.
Main Methods:
- A hybrid algorithm integrating a convolutional neural network (CNN) with random forest regressors and manifold learning was employed.
- Predictors were trained on lesion load location relative to white matter tracts.
- Weighted averaging of predictor outputs, adjusted for EDSS range-specific errors, was used for final prediction.
- The model was trained on 971 MS patients from the OFSEP cohort and validated on an independent set of 475 patients.
Main Results:
- The algorithm achieved a Mean Squared Error (MSE) of 2.2 on the validation dataset.
- The model demonstrated a MSE of 3, with a mean EDSS error of 1.7, on the independent test dataset.
- The developed method successfully predicted two-year clinical disability using FLAIR MRI and basic demographics.
Conclusions:
- The proposed algorithm effectively predicts two-year clinical disability in multiple sclerosis patients with a mean EDSS error of 1.7.
- This predictive model, utilizing readily available FLAIR MRI and demographic data, shows potential for forecasting EDSS score progression.
- Further validation on external cohorts is recommended to confirm the generalizability and robustness of the findings.
