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Abstract Diagnosing language disorders associated with autism is a complex challenge, often hampered by the subjective nature and variability of traditional assessment methods. In this study, we explored large language models (LLMs) to overcome the speed and precision obstacles by enhancing sensitivity and profiling linguistic features for autism diagnosis. This research utilizes natural language understanding capabilities of LLMs to simplify and improve the diagnostic process, focusing on identifying autism-related language patterns. We showed that the proposed method demonstrated improvements over the baseline models, with over a 10% increase in both sensitivity and positive predictive value in a zero-shot learning configuration. Combining accuracy and applicability, the framework could serve as a valuable supplementary tool within the diagnostic process for ASD-related language patterns. We identified ten key features of autism-associated language disorders across scenarios. Features such as echolalia, pronoun reversal, and atypical language usage play a critical role in diagnosing ASD and informing tailored treatment plans. 

IntroductionDiagnosing Autism Spectrum Disorder (ASD) in verbally fluent individuals based on speech patterns in examiner-patient dialogues is challenging because speech-related symptoms are often subtle and heterogeneous. This study aimed to identify distinctive speech characteristics associated with ASD by analyzing recorded dialogues from the Autism Diagnostic Observation Schedule (ADOS-2). MethodsWe analyzed examiner-participant dialogues from ADOS-2 Module 4 and extracted 40 speech-related features categorized into intonation, volume, rate, pauses, spectral characteristics, chroma, and duration. These acoustic and prosodic features were processed using advanced speech analysis tools and used to train machine learning models to classify ASD participants into two subgroups: those with and without A2-defined speech pattern abnormalities. Model performance was evaluated using cross-validation and standard classification metrics. ResultsUsing all 40 features, the support vector machine (SVM) achieved an F1-score of 84.49%. After removing Mel-Frequency Cepstral Coefficients (MFCC) and Chroma features to focus on prosodic, rhythmic, energy, and selected spectral features aligned with ADOS-2 A2 scores, performance improved, achieving 85.77% accuracy and an F1-score of 86.27%. Spectral spread and spectral centroid emerged as key features in the reduced set, while MFCC 6 and Chroma 4 also contributed significantly in the full feature set. DiscussionThese findings demonstrate that a compact, diverse set of non-MFCC and selected spectral features effectively characterizes speech abnormalities in verbally fluent individuals with ASD. The approach highlights the potential of context-aware, data-driven models to complement clinical assessments and enhance understanding of speech-related manifestations in ASD. 

IntroductionEarly and accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective intervention, yet it remains a significant challenge due to its complexity and variability. Micro-expressions are rapid, involuntary facial movements indicative of underlying emotional states. It is unknown whether micro-expression can serve as a valid bio-marker for ASD diagnosis. MethodsThis study introduces a novel machine-learning (ML) framework that advances ASD diagnostics by focusing on facial micro-expressions. We applied cutting-edge algorithms to detect and analyze these micro-expressions from video data, aiming to identify distinctive patterns that could differentiate individuals with ASD from typically developing peers. Our computational approach included three key components: (1) micro-expression spotting using Shallow Optical Flow Three-stream CNN (SOFTNet), (2) feature extraction via Micron-BERT, and (3) classification with majority voting of three competing models (MLP, SVM, and ResNet). ResultsDespite the sophisticated methodology, the ML framework's ability to reliably identify ASD-specific patterns was limited by the quality of video data. This limitation raised concerns about the efficacy of using micro-expressions for ASD diagnostics and pointed to the necessity for enhanced video data quality. DiscussionOur research has provided a cautious evaluation of micro-expression diagnostic value, underscoring the need for advancements in behavioral imaging and multimodal AI technology to leverage the full capabilities of ML in an ASD-specific clinical context. 

Abstract Diagnosing language disorders associated with autism is a complex and nuanced challenge, often hindered by the subjective nature and variability of traditional assessment methods. Traditional diagnostic methods not only require intensive human effort but also often result in delayed interventions due to their lack of speed and specificity. In this study, we explored the application of ChatGPT, a state-of-the-art large language model, to overcome these obstacles by enhancing diagnostic accuracy and profiling specific linguistic features indicative of autism. Leveraging ChatGPT's advanced natural language processing capabilities, this research aims to streamline and refine the diagnostic process. Specifically, we compared ChatGPT's performance with that of conventional supervised learning models, including BERT, a model acclaimed for its effectiveness in various natural language processing tasks. We showed that ChatGPT substantially outperformed these models, achieving over 13\% improvement in both accuracy and F1-score in a zero-shot learning configuration. This marked enhancement highlights the model’s potential as a superior tool for neurological diagnostics. Additionally, we identified ten distinct features of autism-associated language disorders that vary significantly across different experimental scenarios. These features, which included echolalia, pronoun reversal, and atypical language usage, were crucial for accurately diagnosing ASD and customizing treatment plans. Together, our findings advocate for adopting sophisticated AI tools like ChatGPT in clinical settings to assess and diagnose developmental disorders. Our approach not only promises greater diagnostic precision but also aligns with the goals of personalized medicine, potentially transforming the evaluation landscape for autism and similar neurological conditions.