arXiv subjects

Isabel Dziobek

Publications and source records attributed to Isabel Dziobek.

3 recordsLinked to original sources

arXiv2026

What Clinicians Need: Designing, Developing and Evaluating an AI-Based Decision Support System for Autism Assessment

AI methods promise to support autism spectrum condition (ASC) diagnostics in adults, a complex and time-consuming process, that is characterized by a shortage of specialized clinicians. To date, clinicians' needs and their interaction with such AI-based support remain underexplored. Our work aims to develop and evaluate an AI-based clinical decision support system (CDSS) for ASC assessment, and to investigate how it impacts clinicians' decision-making. By interviewing clinicians of varying experience levels, we identified five challenges and derived design strategies. Based on that, we developed SIT-CARE, a CDSS, which provides AI-based recommendations and data visualizations of clinically relevant nonverbal behavior. Through an evaluation study with newly recruited clinicians, we found that SIT-CARE led to different decision paths in regard to the ASC assessment, which are reflected in clinicians' mental models and decision changes. Overall, SIT-CARE demonstrated potential in improving initial diagnostic assessments, supporting in-depth diagnosis and empowering less experienced clinicians.

cs.HC↗

arXiv2026

Video-based Social Interaction Behavior Analysis with the Simulated Interaction Task for Children (Kids-SIT)

Accurately quantifying children's social interaction behavior is part of understanding their cognitive and emotional development, as well as mental health conditions. Kids-SIT is a web-based tool designed to computationally analyse children's behaviors by engaging them in a standardized video conversation while their responses are video recorded. In a pre-registered study with 21 healthy children and 12 children diagnosed with social anxiety disorder (SAD), aged 9-14 years, we assess its potential as an accessible paradigm for automated analysis of children's social interaction behavior. We evaluate whether the Kids-SIT can elicit naturalistic interaction patterns in healthy children, and how well automatic feature extraction methods can detect these patterns. We analyse children's subjective impressions, verbal responses, and non-verbal behaviors. Non-verbal behaviors were manually annotated and, independently, automatically extracted using state-of-the-art methods. In an exploratory analysis, we further assess whether automatically extracted features can distinguish between children with and without SAD. Verbal responses and post-hoc impressions indicate that the Kids-SIT elicits natural social interaction behavior. Non-verbal behavior aligned with this pattern: children looked at their interaction partner most of the time, particularly while listening rather than speaking. Smiling and gazing toward the partner occurred more frequently during the person-directed liked and disliked parts of the conversation than during the picture-description phase. These patterns were captured by both annotations and computational methods. Automatically extracted features enabled above-chance differentiation between children with and without SAD. Our results underscore the potential of the Kids-SIT for analysing children's social interaction behavior, with applicability extending to clinical contexts.

cs.HC↗

arXiv2025

Improving Autism Detection with Multimodal Behavioral Analysis

Due to the complex and resource-intensive nature of diagnosing Autism Spectrum Condition (ASC), several computer-aided diagnostic support methods have been proposed to detect autism by analyzing behavioral cues in patient video data. While these models show promising results on some datasets, they struggle with poor gaze feature performance and lack of real-world generalizability. To tackle these challenges, we analyze a standardized video dataset comprising 168 participants with ASC (46% female) and 157 non-autistic participants (46% female), making it, to our knowledge, the largest and most balanced dataset available. We conduct a multimodal analysis of facial expressions, voice prosody, head motion, heart rate variability (HRV), and gaze behavior. To address the limitations of prior gaze models, we introduce novel statistical descriptors that quantify variability in eye gaze angles, improving gaze-based classification accuracy from 64% to 69% and aligning computational findings with clinical research on gaze aversion in ASC. Using late fusion, we achieve a classification accuracy of 74%, demonstrating the effectiveness of integrating behavioral markers across multiple modalities. Our findings highlight the potential for scalable, video-based screening tools to support autism assessment.

cs.CV↗