Early Diagnosis Of Autism Spectrum Disorder Research Articles

Evaluation of neurotransmitters (Gamma-aminobutyric acid and serotonin) and apoptosis factor (Caspase-7) as biomarkers for the diagnosis of autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disability characterized by impaired communication and social skills along with restricted interests and repetitive behaviors. Biomarkers such as γ-aminobutyric acid (GABA), serotonin (5-Hydroxytryptamine, 5-HT) and caspase-7, showing potential for early diagnosis and management of ASD. To evaluate the effectiveness of GABA, serotonin and caspase-7 as novel biomarkers to predict autism at an early stage. A case- control study which started from November 2023 to June 2024, that involved 77 children’s patients, newly diagnosed with autism. The control group was comprised of 61 age- and sex-matched apparently healthy children, they had no history of autism and any other chronic diseases. Serum samples were analyzed for GABA, serotonin and caspase-7 by using enzyme linked immune-sorbent assay (ELISA) technique. The statistical package for social sciences (SPSS- 26) was used for data analyses. The study revealed significantly elevated GAMA and caspase-7 levels; while, no significant differences of serotonin level between the autistic children’s patients and the control group. This study provides a groundbreaking revelation of the biochemical markers (GAMA and caspase-7) and autism spectrum disorder, the myriad metabolic and neurochemical dysfunctions of which form a complex tapestry of ASD.

Prevalence, incidence, and characteristics of autism spectrum disorder among children in Beijing, China.

It is the first study to explore the prevalence, incidence, and co-occurring conditions of autism spectrum disorder for the preschoolers in China. The prevalence and incidence of autism spectrum disorder has increased in recent decades. Autism spectrum disorder has become an important public concern worldwide. In this study, all hospital confirmed cases had an associated diagnosis (International Classification of Diseases, 10th revision (ICD-10) codes: F84.0, 84.5, F84.9). In total, 4457 children aged 4-6 years were identified as having autism spectrum disorder. In 2021, 1 in 95 children aged 6 years, 1 in 115 children aged 5 years, and 1 in 130 children aged 4 years were estimated to have autism spectrum disorder in Beijing. The incidence was 0.11% in 2019 and increased to 0.18% in 2021. There has been a great emphasis on the importance of early autism spectrum disorder diagnosis in large cities in China.

Advancing ASD identification with neuroimaging: a novel GARL methodology integrating Deep Q-Learning and generative adversarial networks

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects an individual's behavior, speech, and social interaction. Early and accurate diagnosis of ASD is pivotal for successful intervention. The limited availability of large datasets for neuroimaging investigations, however, poses a significant challenge to the timely and precise identification of ASD. To address this problem, we propose a breakthrough approach, GARL, for ASD diagnosis using neuroimaging data. GARL innovatively integrates the power of GANs and Deep Q-Learning to augment limited datasets and enhance diagnostic precision. We utilized the Autistic Brain Imaging Data Exchange (ABIDE) I and II datasets and employed a GAN to expand these datasets, creating a more robust and diversified dataset for analysis. This approach not only captures the underlying sample distribution within ABIDE I and II but also employs deep reinforcement learning for continuous self-improvement, significantly enhancing the capability of the model to generalize and adapt. Our experimental results confirmed that GAN-based data augmentation effectively improved the performance of all prediction models on both datasets, with the combination of InfoGAN and DQN's GARL yielding the most notable improvement.

A DNN-based diagnosis on autism spectrum disorder in children

The prevalence of autism spectrum disorder (ASD) witnesses a sharp increasing in recent years, and early diagnosis and intervention of ASD are critically needed. This study explored the efficacy of Deep Neural Networks (DNN) in diagnosing ASD among children aged 0 to 10. Utilizing the latest dataset derived from the ASDTests mobile application, which encompasses behavioral characteristics of over 2,000 children, we implemented a DNN model to capture complex non-linear patterns indicative of ASD. The results of comparative analysis with traditional machine learning models revealed DNN's superior accuracy in predicting ASD, indicating that the DNN achieved a significant improvement in identifying minority classes post-imbalance learning treatment. The promising results, including the 99.55% accuracy rate, paved the way for future investigations into integrating DNN with multimodal data analysis and other advanced algorithms to enhance early diagnostic processes and intervention strategies for ASD.

Retinal Image Analysis for Simultaneous Classification and Severity Grading of Attention-Deficit Hyperactivity Disorder and Autism Spectrum Disorder using Deep Learning

Over the last 20 years, the number of children born with Autism Spectrum Disorder (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) has significantly increased. According to the U.S Centers for Disease Control and Prevention, the number of children with ASD in the United States had increased from 1 in 150 in 2000 to 1 in 36 in 2020, which is an increase of 316.7% and the number of children with ADHD has been around 10% of Population. As the number of children with ASD and ADHD is increasing rapidly, an early diagnosis of both mental disorders is essential, which can contribute to the improvement of the condition of children significantly. By employing retinal images, the early detection of ASD and ADHD in the developmental stages of childhood becomes feasible. Consequently, the development of a diagnostic system that identifies the presence of ASD and ADHD is essential for healthcare interventions and the enhancement of children's well-being when potential remediation is attainable. This research aims to develop a system for early diagnosis of ASD and ADHD utilizing retinal images and deep learning through convolutional neural networks. The proposed approach classifies the retinal images into disorder categories and severity levels. Experimental results demonstrate the viability of the proposed approach as a biometric for the early diagnosis of ASD and ADHD.

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.

Exploring the Neurogenetic Landscape of Autism Spectrum Disorder: The Role of Brain-Derived Neurotrophic Factor (BDNF) Gene in the Complex Web of Neurodevelopment

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with varied manifestations including altered metabolic profiles. Recent research highlights the potential role of Brain-Derived Neurotrophic Factor (BDNF), a hormone derived from adipose tissue, in the pathology of ASD. BDNF levels are reportedly correlated with the severity of autism symptoms, suggesting a link between metabolic dysfunction and the disorder. Objective: This study aims to deepen the understanding of BDNF levels in individuals with ASD and to explore the implications for diagnosis, treatment, and management of the disorder. It seeks to elucidate the relationship between low BDNF levels and increased autism severity, and to identify potential therapeutic interventions targeting BDNF dysregulation. Methods: EDTA blood samples (5ml each) from 140 participants (108 with ASD and 32 healthy controls) were collected across various hospitals in Karachi. Plasma serum levels of BDNF were analyzed following a six-month follow-up. A comprehensive statistical analysis was performed, focusing on the relationship between BDNF levels and autism severity, and evaluating diagnostic and therapeutic implications of BDNF dysregulation. Results: Analysis revealed that lower BDNF serum levels were consistently associated with higher autism severity. Specifically, 60-70% of participants with ASD scored below 8 on the Mini-Mental State Examination (MMSE), indicating significant cognitive impairment. The statistical significance of these findings was confirmed with p-values <0.05. Conclusion: The study confirms that low BDNF levels are significantly associated with greater severity of autism symptoms, underscoring the importance of metabolic factors in the pathogenesis of ASD. BDNF stands out as a potential biomarker for early ASD diagnosis and personalized treatment strategies. Future Prospects: Future research should aim to delineate the mechanistic relationship between BDNF dysregulation and ASD pathology. Longitudinal studies are needed to assess the long-term effects of BDNF modulation on ASD outcomes and to validate its effectiveness as both a diagnostic and prognostic marker.

Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification.

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

Innovative Strategies for Early Autism Diagnosis: Active Learning and Domain Adaptation Optimization.

The early diagnosis of autism spectrum disorder (ASD) encounters challenges stemming from domain variations in facial image datasets. This study investigates the potential of active learning, particularly uncertainty-based sampling, for domain adaptation in early ASD diagnosis. Our focus is on improving model performance across diverse data sources. Utilizing the Kaggle ASD and YTUIA datasets, we meticulously analyze domain variations and assess transfer learning and active learning methodologies. Two state-of-the-art convolutional neural networks, Xception and ResNet50V2, pretrained on distinct datasets, demonstrate noteworthy accuracies of 95% on Kaggle ASD and 96% on YTUIA, respectively. However, combining datasets results in a modest decline in average accuracy, underscoring the necessity for effective domain adaptation techniques. We employ uncertainty-based active learning to address this, which significantly mitigates the accuracy drop. Xception and ResNet50V2 achieve 80% and 79% accuracy when pretrained on Kaggle ASD and applying active learning on YTUIA, respectively. Our findings highlight the efficacy of uncertainty-based active learning for domain adaptation, showcasing its potential to enhance accuracy and reduce annotation needs in early ASD diagnosis. This study contributes to the growing body of literature on ASD diagnosis methodologies. Future research should delve deeper into refining active learning strategies, ultimately paving the way for more robust and efficient ASD detection tools across diverse datasets.

Using Machine Learning for Motion Analysis to Early Detect Autism Spectrum Disorder: A Systematic Review

AbstractDiagnosis of autism spectrum disorder (ASD) is typically performed using traditional tools based on behavioral observations. However, these diagnosis methods are time-consuming and can be misleading. Integrating machine learning algorithms with technological screening tools within the typical behavioral observations can possibly enhance the traditional assessment and diagnostic process. In the last two decades, to improve the accuracy and reliability of autism detection, many clinicians and researchers began to develop new screening methods by means of advanced technology like machine learning (ML). These methods include artificial neural networks (ANN), support vector machines (SVM), a priori algorithms, and decision trees (DT). Mostly, these methods have been applied to pre-existing datasets, derived from the standard diagnostic and assessment tools, to implement and test predictive models. On the other hand, the detection of new objective behavioral measures such as biomarkers could lead to a significant strengthening of existing screening tools. In the present study, we carried out a critical review of the literature about the latest findings in this field. The aim was to shed light about the effectiveness of using ML systems for motion analysis to enhance both clinical assessment and diagnostic processes. Specifically, we discussed the contribution of ML systems in promoting early diagnosis of ASD. The literature review showed that motion patterns ML analysis predicts ASD classification as accurately as that of classical gold standard tools. However, the application of these methods is still challenging, as discussed in this review.

Deep Learning for Neurodevelopmental Disorder Diagnosis: Leveraging Retinal Images with Self-supervised Learning

Neurodevelopmental disorders like autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have posed substantial challenges to the cognitive, social, and emotional development of children. Early and precise diagnosis of ASD and ADHD holds significant importance in facilitating timely interventions and support, ultimately enhancing outcomes for individuals affected by these conditions. Conventional diagnostic approaches for these disorders have depended on behavioral assessments, clinical observations, and structured interviews, which are labor-intensive and susceptible to subjective judgments made by healthcare professionals. In this research paper, we proposed a deep learning-based system for diagnosing neurodevelopmental disorders. The proposed system utilizes retinal images as input and generates disorder predictions. We introduce a novel self-supervised learning approach to attain superior accuracy outcomes. We demonstrated that the proposed system achieves state-of-the-art accuracy on large-scale retinal image datasets with extensive experimental results.

EASDM: Explainable Autism Spectrum Disorder Model Based on Deep Learning

A neuro-developmental disorder known as autism spectrum disorder (ASD) affects a significant portion of the global population. Those with ASD frequently struggle to interact and communicate with others and may engage in restricted or repetitive behaviors or interests. The symptoms of autism begin early in childhood and can continue into adulthood. Machine learning and deep learning (DL) models are employed in clinical research for the early identification and diagnosis of ASD. However, the majority of the existing models lack interpretability in their results for ASD diagnosis. The explainable artificial intelligence (XAI) concepts can be used to provide transparent and understandable explanations for models’ decisions. In this work, we present an explainable autism spectrum disorder model based on DL for autism disorder detection in toddlers and children. The primary objective of this study is to better understand and interpret the classification process and to discern the significant features that contribute to the prediction of ASD. The proposed model is divided into two distinct components. The first component employs a DL model for autism disorder detection. The second uses an XAI technique known as shapley additive explanations (SHAP) to emphasis key characteristics and explain the model’s outcomes. The model showed perfect performance on the training set, with an accuracy of 1 and a receiver operating characteristic score of 1. On the test set, the model achieved an accuracy score of 0.9886, indicating that it performed nearly as well as on the training set. The experimental results demonstrate that the proposed model has the capability to accurately predict and diagnose ASD while also providing explanatory insights into the obtained results. Furthermore, the results indicate that the proposed model performs competitively compared to the state-of-the-art models in terms of accuracy and F1-score. The results highlight the efficacy and potential of the proposed model in accurately predicting ASD in binary classification tasks.

Validation of preparedness and willingness on early detection of autism spectrum disorder among healthcare providers in Jordan

In recent years, there have been significant advancements in the field of early detection and intervention for children with developmental and behavioural disorders, with significant in the context of Autism Spectrum Disorder (ASD). It highlights the advantages of early detection, such as reduced long-term costs and positive effects on a child's development. However, it also acknowledges the prevalent barriers to early ASD diagnosis and intervention. This study aims to develop and validate questionnaires on Preparedness and Willingness in early ASD detection among Al-Mafraq City, Jordan, healthcare providers. The research in the Al-Mafraq region of Jordan employed a cross-sectional research design. The study first involved the development of a questionnaire, which underwent rigorous content validation by a panel of experts well-versed in ASD. Face validity was assessed utilizing the Face Validity Index (FVI). A random sample of 130 healthcare providers from one hospital answered the draft questionnaires online for the EFA analysis recommended. It resulted in satisfactory items' factor loadings (r>0.60) and internal reliability (Cronbach's alpha >0.8). Followed by CFA from a random sample of another 202 healthcare providers from another hospital and healthcare centres in Al-Mafraq showed that the instrument effectively met the validation criteria, demonstrating its reliability and validity in assessing healthcare providers' Preparedness and Willingness for early ASD detection. Notably, the participants in the study exhibit differing degrees of Preparedness and Willingness. In conclusion, the study underscores the critical standing of developing a comprehensive instrument to evaluate the limitations associated with early ASD detection among healthcare providers.

The Association Between Branched-Chain Amino Acid Concentrations and the Risk of Autism Spectrum Disorder in Preschool-Aged Children

Several studies have linked branched-chain amino acid (BCAA) metabolism disorders with autism spectrum disorder (ASD), but the results have been inconsistent. The purpose of this study was to explore the association between BCAA concentrations and the risk of ASD. A total of 313 participants were recruited from two tertiary referral hospitals from May 2018 to July 2021. Concentrations of BCAAs in dried blood spots were analyzed using liquid chromatography-tandem mass spectrometry-based analysis. Multivariate analyses and restricted cubic spline models were used to identify the association between BCAAs and the risk of ASD, and a nomogram was developed by using multivariate logistic regression and the risk was determined by receiver operating characteristic curve analysis and calibration curve analysis. Concentrations of total BCAA, valine, and leucine/isoleucine were higher in the ASD group, and all of them were positively and non-linearly associated with the risk of ASD even after adjusting for potential confounding factors such as age, gender, body mass index, and concentrations of BCAAs (P < 0.05). The nomogram integrating total BCAA and valine showed a good discriminant AUC value of 0.756 (95% CI 0.676–0.835). The model could yield net benefits across a reasonable range of risk thresholds. In the stratified analysis, the diagnostic ability of the model was more pronounced in children older than 3 years. We provide evidence that increased levels of BCAAs are associated with the risk of ASD, and the nomogram model of BCAAs presented here can serve as a marker for the early diagnosis of ASD.

Real-time facial emotion recognition model based on kernel autoencoder and convolutional neural network for autism children

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is characterized by abnormalities in the brain, leading to difficulties in social interaction and communication, as well as learning and attention. Early diagnosis of ASD is challenging as it mainly relies on detecting abnormalities in brain function, which may not be evident in the early stages of the disorder. Facial expression analysis has shown promise as an alternative and efficient solution for early diagnosis of ASD, as children with ASD often exhibit distinctive patterns that differentiate them from typically developing children. Assistive technology has emerged as a crucial tool in improving the quality of life for individuals with ASD. In this study, we developed a real-time emotion identification system to detect the emotions of autistic children in case of pain or anger. The emotion recognition system consists of three stages: face identification, facial feature extraction, and feature categorization. The proposed system can detect six facial emotions: anger, fear, joy, natural, sadness, and surprise. To achieve high-performance accuracy in classifying the input image efficiently, we proposed a deep convolutional neural network (DCNN) architecture for facial expression recognition. An autoencoder was used for feature extraction and feature selection, and a pre-trained model (ResNet, MobileNet, and Xception) was applied due to the size of the dataset. The Xception model achieved the highest performance, with an accuracy of 0.9523%, sensitivity of 0.932, specificity of 0.9421, and AUC of 0.9134%. The proposed emotion detection framework leverages fog and IoT technologies to reduce latency for real-time detection with fast response and location awareness. Using fog computing is particularly useful when dealing with big data. Our study demonstrates the potential of using facial expression analysis and deep learning algorithms for real-time emotion recognition in autistic children, providing medical experts and families with a valuable tool for improving the quality of life for individuals with ASD.

Virtual Reality Technology for Early Detection and Diagnosis of Autism Spectrum Disorder

The presence of Autism Spectrum Disorder (ASD) in a person makes them lag in social interaction. Thus, early detection and diagnosis solution is needed for ASD patients. Hence, an SFP-ANFIS-based VR intervention for the early diagnosis of ASD is proposed. Initially, the EEG signal of an ASD patient is taken and pre-processed, from which features are extracted. Based on the extracted features, the SH-KPA groups the patients with autism and their corresponding emotion. Similarly, from the behavioral images, the features are extracted after landmarking the facial points. Then, the features are classified with SH-KPA. From the SH-KPA results of EEG signal and behavioral images, the similarity is estimated to recognize the ASD child emotion. Then, based on the previous health records of patients, the state of the patient, estimated emotions, and similarity score, the SFP-ANFIS predicts the level of ASD and recommends a possible solution to be conducted with a VR device. Finally, through experiments, the efficacy of the proposed model is proved.

Factors Associated with Age at Diagnosis of Autism Spectrum Disorder in Pediatric Patients at Sawanpracharak Hospital, Thailand

Objective: This study aimed to determine the average age at diagnosis and the characteristics associated with the age of children at the diagnosis of autism spectrum disorder (ASD) at Sawanpracharak Hospital. Materials and Methods: A retrospective cross-sectional study was conducted from May 2023 to July 2023. Data were collected from the medical patient files of all children diagnosed with ASD between 2020 to 2022. Descriptive analysis was used to examine the characteristics of the children and their caregivers, and the children’s age at diagnosis. Factors associated with the age at diagnosis were assessed by chi-square test analysis. Results: In total, 100 patient records with complete information were collected. The average age at the diagnosis of ASD was 4.57± 1.61 years old, with 60% of the patients diagnosed after four years of age. Social communication deficit symptoms, including non-response to name and lack of pointing out objects of interest, were significantly associated with an early ASD diagnosis (p-value = 0.023 and 0.002, respectively). Being a firstborn child and the presence of delayed development were found to delay the diagnosis of ASD meaning it occurred at a later age (p-value = 0.002 and 0.019, respectively). However, sex, the caregiver’s education, and socioeconomic status were not related to the age at diagnosis. Conclusion: Most children with ASD who received treatment at Sawanpracharak Hospital were diagnosed late. Being a firstborn child, poor response to name being called, lack of pointing out objects of interest, and delayed development were related to the age of the children at ASD diagnosis. Differences in diagnostic age based on sociodemographic and clinical characteristics indicate the need for coordinated measures for the early detection of ASD.

Identification of Early Clinical Features of Autism Spectrum Disorders in Bangladeshi Children- A Cross Sectional Study

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopment disorders with a heterogeneous spectrum of clinical symptomatology related to social interaction and communication. Autism usually appears in children within first three years of life during maximum neuronal development of brain. Several studies showed that children diagnosed before age three had the least stable diagnoses and the most positive outcomes. They explain that early diagnosis of ASD in children and early intervention (i.e., before age three years) appear to have the greatest benefit regarding the disappearance of core symptoms. Therefore, it could be possible to minimize the severity of ASD which lead to our interest to identify the early clinical features of autism spectrum disorder in Bangladeshi children.

Unveiling the interplay between NSAID-induced dysbiosis and autoimmune liver disease in children: insights into the hidden gateway to autism spectrum disorders. Evidence from ex vivo, in vivo, and clinical studies.

Autism spectrum disorders (ASD) represent a diverse group of neuropsychiatric conditions, and recent evidence has suggested a connection between ASD and microbial dysbiosis. Immune and gastrointestinal dysfunction are associated with dysbiosis, and there are indications that modulating the microbiota could improve ASD-related behaviors. Additionally, recent findings highlighted the significant impact of microbiota on the development of autoimmune liver diseases, and the occurrence of autoimmune liver disease in children with ASD is noteworthy. In the present study, we conducted both an in vivo study and a clinical study to explore the relationship between indomethacin-induced dysbiosis, autoimmune hepatitis (AIH), and the development of ASD. Our results revealed that indomethacin administration induced intestinal dysbiosis and bacterial translocation, confirmed by microbiological analysis showing positive bacterial translocation in blood cultures. Furthermore, indomethacin administration led to disturbed intestinal permeability, evidenced by the activation of the NLRP3 inflammasomes pathway and elevation of downstream biomarkers (TLR4, IL18, caspase 1). The histological analysis supported these findings, showing widened intestinal tight junctions, decreased mucosal thickness, inflammatory cell infiltrates, and collagen deposition. Additionally, the disturbance of intestinal permeability was associated with immune activation in liver tissue and the development of AIH, as indicated by altered liver function, elevated ASMA and ANA in serum, and histological markers of autoimmune hepatitis. These results indicate that NSAID-induced intestinal dysbiosis and AIH are robust triggers for ASD existence. These findings were further confirmed by conducting a clinical study that involved children with ASD, autoimmune hepatitis (AIH), and a history of NSAID intake. Children exposed to NSAIDs in early life and complicated by dysbiosis and AIH exhibited elevated serum levels of NLRP3, IL18, liver enzymes, ASMA, ANA, JAK1, and IL6. Further, the correlation analysis demonstrated a positive relationship between the measured parameters and the severity of ASD. Our findings suggest a potential link between NSAIDs, dysbiosis-induced AIH, and the development of ASD. The identified markers hold promise as indicators for early diagnosis and prognosis of ASD. This research highlights the importance of maintaining healthy gut microbiota and supports the necessity for further investigation into the role of dysbiosis and AIH in the etiology of ASD.