In the context of the H2020 BRAINTEASER project, our group is in charge of developing predictive models for the progression of Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). In order to identify the most promising approaches to be implemented, we coordinated a systematic review of the artificial intelligence (AI) methodological landscape in ALS, focusing on patient stratification and disease progression prediction, which we performed together with the other project partners.
Out of 1604 reports, we identified 15 studies on patient stratification, 28 on ALS progression prediction, and 6 on both. We highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.
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These findings laid the groundwork for the development of our models within the project, providing valuable insights into the most effective AI methodologies for patient stratification and disease progression prediction in ALS. They are also guiding our direction in identifying key areas for further development and refinement.
