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Motivation: Behavioral observations are an important resource in the studyand evaluation of psychological phenomena, but it is costly, time-consuming,and susceptible to bias. Thus, we aim to automate coding of human behavior foruse in psychotherapy and research with the help of artificial intelligence (AI)tools. Here, we present an analysis plan. Methods: Videos of a gold-standardsemi-structured diagnostic interview of 25 youth with obsessive-compulsivedisorder (OCD) and 12 youth without a psychiatric diagnosis (no-OCD) will beanalyzed. Youth were between 8 and 17 years old. Features from the videos willbe extracted and used to compute ratings of behavior, which will be compared toratings of behavior produced by mental health professionals trained to use aspecific behavioral coding manual. We will test the effect of OCD diagnosis onthe computationally-derived behavior ratings using multivariate analysis ofvariance (MANOVA). Using the generated features, a binary classification modelwill be built and used to classify OCD/no-OCD classes. Discussion: Here, wepresent a pre-defined plan for how data will be pre-processed, analyzed andpresented in the publication of results and their interpretation. A challengefor the proposed study is that the AI approach will attempt to derivebehavioral ratings based solely on vision, whereas humans use visual,paralinguistic and linguistic cues to rate behavior. Another challenge will beusing machine learning models for body and facial movement detection trainedprimarily on adults and not on children. If the AI tools show promisingresults, this pre-registered analysis plan may help reduce interpretation bias.Trial registration: ClinicalTrials.gov - H-18010607

Computational behavior recognition in child and adolescent psychiatry: A statistical and machine learning analysis plan