Can Automatic Facial Expression Analysis Be Used for Treatment Outcome Estimation in Schizophrenia?

Negative symptoms of schizophrenia include expressive deficits that are marked by a reduction in patients' behaviour. Analysing automatically non-verbal behaviour and exploiting the results for estimating symptom severity has drawn attention recently. However, those approaches are not accurate enough to be used for monitoring the changes in patient's symptom level during treatment interventions (i.e. the treatment outcome). In this paper, we propose a method that directly addresses the problem of Treatment Outcome Estimation (TOE) in schizophrenia - more specifically, is aimed at determining whether specific symptoms have improved or not by analysing jointly two videos of the same patient, one before and one after the treatment. The proposed architecture builds on Recurrent Neural Networks (RNNs) that learn differences in the patient behaviour before and after treatment. We validate the method in videotaped interviews for symptom assessment for 74 patients. Experimental results show that the proposed architecture achieves promising results for TOE in two different symptom assessment scales.