Non-Verbal Approach to Screening Diagnostics of Depression and Anxiety

Abstract

In a range of clinical situations evaluation of depression and anxiety is important but problematic. For instance, depression and anxiety are risk factors of poor outcomes of cardiac operations. However, verbal approaches to the diagnostics of depression and anxiety commonly increases nervousness in patients. Therefore, we undertook two studies in order to develop a non-verbal tool for screening diagnostics of depression and anxiety. The first study included 33 patients awaiting on-pump operations. The second study included 62 neurotic patients. Szondi portraits were used for evaluation of facial perception. We asked subjects to select one of two responses concerning mood of a person at the portrait: good versus bad mood. Then we asked subjects to select one of responses concerning friendliness of a person at the same portrait: friendly versus unfriendly. Cardiac surgery patients with moderate depression selected significantly more 'bad mood' and 'unfriendly' responses in comparison with non-depressed patients awaiting operation. Facial perception task allowed to correctly classify 84.8% of patients. In addition, the patients showed significant decrease of 'bad mood' responses at discharge in comparison with preoperative period. The results of the second study did not confirm the Szondi portraits task to be an efficient tool for the screening diagnosis of depression. Nevertheless, we observed a range of significant associations between severity of depression or anxiety and performance on the facial perception task. We concluded that facial perception tests are promising tools for nonverbal screening diagnostics of depression and anxiety, and further research in this field is needed.

Depression screening using a non-verbal self-association task: A machine-learning based pilot study

“…Given the limitations of existing assessments, non-verbal alternatives of depression screening are desirable, and may complement and facilitate screening accuracy of verbal assessments. Although not widely adopted in clinical settings, various non-verbal depression screening methods have already been explored, including facial perception behavioural assessment (Guekht et al, 2013), eye gaze pattern analysis (Alghowinem et al, 2015), facial visual features and voice analysis (Pampouchidou et al, 2019;Scibelli et al, 2018), and there's a plethora of research on identifying depression using neuroimaging data and machine-learning (ML) (Janssen et al, 2018). A common issue with the non-verbal depression screening methods such as EEG and MRI are the high cost (CostHelper, 2021;Sahu et al, 2020) NON-VERBAL DEPRESSION SCREENING 4 with poor interpretability (Schnack, 2019).…”