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