A proof-of-concept TAD algorithm has the potential to streamline differential diagnoses of ASD in the future, enabling faster and more accurate diagnostic assessment and ultimately reducing patient distress by shortening the wait for an appropriate treatment plan.
Abstract
Abstract Background Diagnosing autism spectrum disorder (ASD) in adulthood is time-consuming and markedly complicated by the requirement to distinguish between ASD and differential diagnoses also associated with social interaction difficulties, such as borderline personality disorder (BPD)—a distinction for which currently no valid screening or diagnostic tool exists. While technology-assisted diagnostics (TAD) has emerged, existing algorithms have focused on classifying between ASD and no diagnosis, not fully addressing clinical reality. Objective Therefore, we assessed the feasibility of TAD for differential diagnostics by classifying between ASD and BPD in this proof-of-concept study. Methods We collected a rich multimodal dataset of reciprocal interactions, specifically dyadic conversations (n=120 interaction partners). From this data, we extracted more than 800 features, allowing us to capture the core area of defining symptoms for both conditions: social interactions. These features include speech patterns, facial expressions, movement quantity and interpersonal synchrony. We used these features to train and stack linear support vector machines to classify between ASD-involved, BPD-involved and comparison interaction partners. Findings Base models capturing facial expressions during speaking and listening, speech patterns, synchronisation of facial expressions and movement quantity all performed above chance when differentiating between ASD-involved and BPD-involved interaction partners. Stacking all base models containing conceptually related features further increased accuracy, with our algorithm achieving nearly 82% of balanced accuracy, solely based on 20 min of conversation. Conclusions Our proof-of-concept study shows the immense potential of TAD for differential diagnostics: data collection only requires microphones and webcams while feature-extraction is automated, making this approach highly objective, scalable and user-friendly. Clinical implications Our TAD algorithm shows the potential of multimodal, behavioural data for differential diagnostics. On the basis of clinical validation such an algorithm has the potential to streamline differential diagnoses of ASD in the future, enabling faster and more accurate diagnostic assessment and ultimately reducing patient distress by shortening the wait for an appropriate treatment plan.
Autism Spectrum Disorder (ASD) is a neurological and developmental condition characterized by challenges in social interaction, communication (both verbal and non-verbal), and repetitive behaviours. While genetics play a key role in its onset, early diagnosis remains essential for effective intervention. Machine learning (ML) offers a promising approach to streamline and accelerate ASD detection, making it faster and more cost-effective than traditional methods. This paper evaluates eight classification models to identify key ASD features and automate diagnosis. We compare their performance on large datasets to enhance predictive accuracy. ML has transformed healthcare by leveraging vast data volumes for analysis, with technological advances over the past decade improving diagnostic tools now standard in medical settings. ASD affects individuals variably, with symptoms typically appearing between 18 months and 3 years. Although genetic and environmental factors contribute, no single cause is confirmed. Traditional screenings rely heavily on clinician expertise, involving manual assessments and scoring, which can be subjective and time-consuming—even experts face uncertainties in predicting onset or severity. Parents seek rapid, reliable results. ML and deep learning (DL) address these gaps by analyzing complex patterns in data, enabling early prediction of ASD and its severity. This study implements diverse algorithms to support precise, automated screening, reducing diagnostic delays and improving outcomes.
Devireddy Mamatha, K. Maheswari· 2026 6th International Confe...· 0 citations
Background: Digital behavioral phenotyping of autism spectrum disorder (ASD) offers a promising approach for developing more scalable diagnostic frameworks across diverse global contexts. Machine learning (ML) models show promise for ASD diagnosis using behavioral videos, but critical questions remain regarding whether models trained on data from one country work in another, and how the background of the raters affects the accuracy. Our work addresses these questions by testing whether ML models can accurately diagnose ASD across different populations and rater groups. Methods: This work evaluates the performance of a supervised ML framework for binary classification of ASD versus non-ASD [speech, language and communication disorders (SLC) + neurotypical (NT)] in a cohort of 227 children in Bangladesh. We first assessed the cross-domain model transferability of a clinical-instrument-trained logistic regression model (LR-9) on behavioral ratings that were based on videos of Bangladeshi children interacting with caregivers and toys at two major child development centers in Dhaka, Bangladesh. We then trained five diverse classifiers (Logistic Regression, Random Forest, XGBoost, SVM, and RuleFit) on the full annotated Bangladeshi dataset. Using SHAP-based consensus elbow feature selection, we identified a compact set of features that maintained the performance. Finally, we developed ensemble models to improve predictive stability. Results: The LR-9 model, originally trained on U.S. clinical instrument data, was evaluated on video-based behavioral ratings from 214 Bangladeshi children. When tested on Bangladeshi clinician ratings, the LR-9 model achieved a sensitivity of 86.1% (95% CI: [0.78–0.93]) and AUC of 0.79 (95% CI: [0.73–0.86]). The distinction across rater groups was between trained raters (clinicians and students) and crowd workers, who showed lower sensitivity 28.5% (95% CI: [0.21, 0.39]). When tested on the aggregated ratings from all groups, the model achieved an AUC of 0.78 (95% CI: [0.72–0.84]). Inter-rater reliability followed the same pattern: individual agreement was fair (Krippendorff’s α = 0.26), but the multi-rater consensus was reliable (ICC(1,k) = 0.84), with Bangladeshi clinicians showing the highest agreement (α = 0.34) and crowd workers the lowest (α = 0.20). We then trained new models directly on the Bangladeshi ratings. All model types achieved similar AUC values (0.86–0.89), with overlapping confidence intervals. Using just 8–11 key behaviors kept the similar performance while cutting the features by 66–75%. Combining ensembles gave similar results (e.g., Bayesian averaging: AUC 0.88 [0.78, 0.95]) but with more stable predictions. Conclusion: This study provides evidence that mobile video-based ASD diagnosis can achieve comparable performance (AUC: 0.89 [0.76, 0.96]) to models trained on clinical instrument data. This work contributes to the development of broader adaptable autism detection tools, bypassing the dependence on traditional clinical instrument data.
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A Hybrid Intelligent Model designed to predict ASD in pediatric cases, leveraging adaptive neuro-fuzzy systems integrates artificial neural network capabilities with fuzzy logic, offering a comprehensive approach to ASD prediction.
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Children and adolescents with ASD exhibited lower empathy capabilities than control subjects, which may be attributed to dysfunctions in the salience and social brain networks.
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The proposed ensemble-based machine learning classifier methodology presented in this study seeks to revolutionize the diagnosis of ASD by harnessing the collective power of various machine learning algorithms to enhance diagnostic precision, mitigate the subjectivity associated with traditional diagnostic methods, and accelerate the detection process.
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With Early detection of Autism Spectrum Disorder (ASD) can make a life-changing difference in a child’s journey,
helping them receive the right support at the right time. This project introduces a smart, hybrid system that combines advanced
deep learning technology with proven treatment methods, aiming to close the gap between diagnosis and meaningful help. It
examines various types of information such as behavioural assessments and sensory response patterns to train a model that can
identify early signs of autism with high accuracy and consistency. When the system detects a possible case, it provides structured,
theory-based activities designed to develop cognitive, social, emotional, and communication skills in young children. These
activities are based on widely accepted approaches and are intended to encourage steady developmental growth. A major strength
of this system is its automated reporting feature, which gathers diagnostic insights, structured treatment recommendations, and
predicted progress into a clear, easy-to-read report for parents, therapists, and healthcare professionals, ensuring everyone stays
informed and aligned. By blending advanced computational analysis with trusted treatment practices, the system ensures both
accurate detection and a smooth path to intervention. It supports early diagnosis, ongoing guidance, and continuous monitoring,
helping reduce delays and improving engagement in a child’s developmental plan. This combined approach demonstrates how
technology and professional expertise can work together to create an accessible, practical tool for ASD management. Its goal is
to transform early detection into immediate, meaningful action that nurtures potential, builds confidence, and helps shape a
brighter future for every child.
S. Ahmed, Shaikh Faeik, Shaikh Israhil et al.· International Journal for Re...· 0 citations