The Expert Knowledge combined with AI outperforms AI Alone in Seizure
  Onset Zone Localization using resting state fMRI
Payal Kamboj Ayan Banerjee Varina L. Boerwinkle Sandeep K. S. Gupta
We evaluated whether integration of expert guidance on seizure onset zone
(SOZ) identification from resting state functional MRI (rs-fMRI) connectomics
combined with deep learning (DL) techniques enhances the SOZ delineation in
patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI
were collected from 52 children with RE who had subsequently undergone ic-EEG
and then, if indicated, surgery for seizure control (n = 25). The resting state
functional connectomics data were previously independently classified by two
expert epileptologists, as indicative of measurement noise, typical resting
state network connectivity, or SOZ. An expert knowledge integrated deep network
was trained on functional connectomics data to identify SOZ. Expert knowledge
integrated with DL showed a SOZ localization accuracy of 84.8& and F1 score,
harmonic mean of positive predictive value and sensitivity, of 91.7%.
Conversely, a DL only model yielded an accuracy of less than 50% (F1 score
63%). Activations that initiate in gray matter, extend through white matter and
end in vascular regions are seen as the most discriminative expert identified
SOZ characteristics. Integration of expert knowledge of functional connectomics
can not only enhance the performance of DL in localizing SOZ in RE, but also
lead toward potentially useful explanations of prevalent co-activation patterns
in SOZ. RE with surgical outcomes and pre-operative rs-fMRI studies can yield
expert knowledge most salient for SOZ identification.