The Resilience Doc is vision A.I. for mental health diagnostics.
Textpert developed technology to observe a video chat via phone or computer and assess your resilience to anxiety and depression.
We wrote a paper* about our 750-person validation study and it was accepted into an APA peer reviewed journal (Psychological Assessment).
A.I. to detect resilience using vision, audio & NLP models
The Resilience Doc (RD) combines predictions using an ensemble neural network — similar to the neural network structure in the human brain.
Inputs from body language, tonality, & speech are analyzed through hidden layers & nodes. The RD identifies patterns then generates Resilience Scores on a 0-100 scale.
The RD detected resilience to depression and anxiety with 90% sensitivity, 80% accuracy & 90% specificity on blind validated models
750 Participants evaluated
The RD can process 20 seconds of extemporaneous conversation and assess resilience to depression and anxiety. Video chats via computer or smartphone can now create useful mental health data
Figure 1: Bing sentiment analysis
Figure 1 Each word uttered during the study was assigned as positive or negative. People with higher depression risk respond with more negative words
Figure 2: Word variance by PHQ-9
Figure 2 The plot displays the most significant words per PHQ-9 score. Depressed individuals utilize more ordinary language and overuse the word "really"
Figure 3: Audio encoding
Figure 3 The graph represents various inflection frequencies from just one sentence, digitized and encoded for audio processing. The RD observes inflection variances and analyzes patterns
Figure 4: Word analysis
Figure 4 The RD analyzes overall sentiment and each word is scored across 250 dimensions. The RD observes patterns and identifies the meaning of each statement