I am an engineer applying neuroimaging methods to answer clinical questions in epilepsy. There is a new review paper out - machine learning applications in epilepsy https://onlinelibrary.wiley.com/doi/full/10.1111/epi.16333
I am particularly interested in section 4.1 as many of the the studies include the typical 50 or so patients and 50 or so controls and hundreds to thousands of imaging parameters per subject used in a ML to classify something like type of lesion (example ref 52: https://n.neurology.org/content/86/7/643). In section 5 there are examples of the same numbers of subjects and parameters used to predict treatment outcome (example: ref 79 “Deep learning applied to whole‐brain connectome to determine seizure control after epilepsy surgery”).
I have heard that with so many parameters, you need many more subjects to use ML methods. Do these types of studies perform other methods that “get around” this issue? These are published in high impact journals and are considered highly in this field. With similar data and similar goals I wonder if this is an approach I should be considering?
Thanks for any thoughts on this.