Use of Computer Vision Tools to Identify Behavioral Markers of Pediatric Obsessive-Compulsive Disorder: A Pilot Study

Automated Quantification of Eye Tics Using Computer Vision and Deep Learning Techniques

BACKGROUND

Tourette syndrome (TS) tics are typically quantified using "paper and pencil" rating scales that are susceptible to factors that adversely impact validity. Video-based methods to more objectively quantify tics have been developed but are challenged by reliance on human raters and procedures that are resource intensive. Computer vision approaches that automate detection of atypical movements may be useful to apply to tic quantification.

OBJECTIVE

The current proof-of-concept study applied a computer vision approach to train a supervised deep learning algorithm to detect eye tics in video, the most common tic type in patients with TS.

METHODS

Videos (N = 54) of 11 adolescent patients with TS were rigorously coded by trained human raters to identify 1.5-second clips depicting "eye tic events" (N = 1775) and "non-tic events" (N = 3680). Clips were encoded into three-dimensional facial landmarks. Supervised deep learning was applied to processed data using random split and disjoint split regimens to simulate model validity under different conditions.

RESULTS

Area under receiver operating characteristic curve was 0.89 for the random split regimen, indicating high accuracy in the algorithm's ability to properly classify eye tic vs. non-eye tic movements. Area under receiver operating characteristic curve was 0.74 for the disjoint split regimen, suggesting that algorithm generalizability is more limited when trained on a small patient sample.

CONCLUSIONS

The algorithm was successful in detecting eye tics in unseen validation sets. Automated tic detection from video is a promising approach for tic quantification that may have future utility in TS screening, diagnostics, and treatment outcome measurement. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The Use of Immersive Environments for the Early Detection and Treatment of Neuropsychiatric Disorders

Neuropsychiatric disorders are highly prevalent conditions with significant individual, societal, and economic impacts. A major challenge in the diagnosis and treatment of these conditions is the lack of sensitive, reliable, objective, quantitative tools to inform diagnosis, and measure symptom severity. Currently available assays rely on self-reports and clinician observations, leading to subjective analysis. As a step toward creating quantitative assays of neuropsychiatric symptoms, we propose an immersive environment to track behaviors relevant to neuropsychiatric symptomatology and to systematically study the effect of environmental contexts on certain behaviors. Moreover, the overarching theme leads to connected tele-psychiatry which can provide effective assessment.

Obsessive-compulsive disorder in children and adolescents: epidemiology, diagnosis and management

Obsessive-compulsive disorder (OCD) can be found in about 4% of the general population and is characterized by various compulsions and obsessions that interfere with the person's quality of life from a mild to severe degree. The following discussion reflects on current concepts in this condition, including its epidemiology and etiologic underpinnings (behavioral, neurological, immunological, gastroenterological, as well as genetic). The interplay of PANS and PANDAS are included in this review. In addition, the core concepts of OCD diagnosis, differential diagnosis, and co-morbidities are considered. It is stressed that the quality of life for persons with pediatric OCD as well as for family members can be quite limited and challenged. Thus, principles of management are presented as a guide to improve the quality of life for these persons as much as possible.