Autism Spectrum Disorder Classification Research Articles

Detecting Autism from Head Movements using Kinesics.

Head movements play a crucial role in social interactions. The quantification of communicative movements such as nodding, shaking, orienting, and backchanneling is significant in behavioral and mental health research. However, automated localization of such head movements within videos remains challenging in computer vision due to their arbitrary start and end times, durations, and frequencies. In this work, we introduce a novel and efficient coding system for head movements, grounded in Birdwhistell's kinesics theory, to automatically identify basic head motion units such as nodding and shaking. Our approach first defines the smallest unit of head movement, termed kine, based on the anatomical constraints of the neck and head. We then quantify the location, magnitude, and duration of kines within each angular component of head movement. Through defining possible combinations of identified kines, we define a higher-level construct, kineme, which corresponds to basic head motion units such as nodding and shaking. We validate the proposed framework by predicting autism spectrum disorder (ASD) diagnosis from video recordings of interacting partners. We show that the multi-scale property of the proposed framework provides a significant advantage, as collapsing behavior across temporal scales reduces performance consistently. Finally, we incorporate another fundamental behavioral modality, namely speech, and show that distinguishing between speaking- and listening-time head movementsments significantly improves ASD classification performance.

WS-BiTM: Integrating White Shark Optimization with Bi-LSTM for Enhanced Autism Spectrum Disorder Diagnosis

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition marked by challenges in social communication, sensory processing, and behavioral regulation. The delayed diagnosis of ASD significantly impedes timely interventions, which can exacerbate symptom severity. With approximately 62 million individuals affected worldwide, the demand for efficient diagnostic tools is critical. This study introduces a novel framework that combines a White Shark Optimization (WSO)-based feature selection method with a Bidirectional Long Short-Term Memory (Bi-LSTM) classifier for enhanced autism classification. Utilizing the WSO technique, we identify key features from autism screening datasets, which markedly improves the model's predictive capabilities. The optimized feature set is then processed by the Bi-LSTM classifier, enhancing its efficiency in handling sequential data. We comprehensively address methodological challenges, including overfitting, generalization, interpretability, and computational efficiency. Furthermore, we conduct a comparative analysis against baseline algorithms such as Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, while also employing Particle Swarm Optimization (PSO) for feature selection validation. We evaluate performance metrics, including accuracy, F1-score, specificity, precision, and sensitivity across three ASD datasets: Toddlers, Adults, and Children. Our results demonstrate that the WS-BiTM model significantly outperforms baseline methods, achieving accuracies of 97.6%, 96.2%, and 96.4% on the respective datasets. Additionally, we implemented leave-one-dataset cross-validation and confirmed the statistical significance of our findings through a paired t-test, supplemented by an ablation study to detail the contributions of individual model components. These findings highlight the potential of the WS-BiTM model as a robust tool for ASD classification.

Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis

BackgroundThe use of the deep learning (DL) approach has been suggested or applied to identify childhood autism spectrum disorder (ASD). The capacity to predict ASD, however, differs across investigations. Our study’s objective was to conduct a meta-analysis to determine the DL for ASD in children’s classification accuracy.MethodsEligibility criteria were designed according to the purpose of the meta-analysis; PubMed, EMBASE, Cochrane Library, and Web of Science Database were searched for articles published up to April 16, 2023, on the accuracy of DL methods for ASD classification. Using the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) to assess the quality of the included studies. Sensitivity, specificity, areas under the curve (AUC), summary receiver operating characteristic (SROC), and corresponding 95% confidence intervals (CIs) were compiled by using the bivariate random-effects models.ResultsA total of 11 predictive trials based on DL models were included, involving 9495 ASD patients from 6 different databases. According to bivariate random-effects models’ results, the overall sensitivity, specificity, and AUC of the DL technique for ASD were, 0.95 (95% CI = 0.88–0.98), 0.93 (95% CI = 0.85–0.97), and 0.98 (95%CI: 0.97–0.99), respectively. Subgroup analysis results found that different datasets did not cause heterogeneity (meta-regression P = 0.55). The Kaggle dataset’s sensitivity and specificity were 0.94 (95%CI: 0.82-1.00) and 0.91 (95%CI: 0.76-1.00), and with 0.97 (95%CI: 0.92-1.00) and 0.97 (95%CI: 0.92-1.00) for ABIDE dataset.ConclusionsDL techniques has satisfactory sensitivity, specificity, and AUC in ASD classification. However, the major heterogeneity of the included studies limited the effectiveness of this meta-analysis. Further trials need to be performed to demonstrate the clinical practicability of DL diagnosis.

Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis.

Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML classification rests, agree with established diagnostic approaches based on clinical symptoms. Furthermore, complex network analyses highlight differences in brain network integration and segregation among the three groups. Our findings pave the way for refined, ML-enhanced diagnostics in accordance with established practices, offering a promising avenue for developing trustworthy clinical decision-support systems.

A Survey on Genetic Disease − Autism Spectrum Disorder Prediction and Classification in Machine Learning

Autism spectrum disorder (ASD) is a hereditary, neurological condition with many aetiologies that manifest in early childhood. Mental illnesses, including anxiety, poor communication, and a lack of recurrent interest, may result from ASD. It can be highly advantageous for children to improve their psychological wellness level if the ASD is recognized in the earlier years of life. Furthermore, machine learning (ML) approaches are now essential for diagnosing and categorising ASD. The creation of computer programmes that can acquire data and utilise it to gain knowledge for oneself is the main goal of this aspect of artificial intelligence. Many scholars have suggested various ML strategies for quickly and accurately detecting the various forms of ASD. This paper presents a survey on ASD prediction and classification using ML methods-based research articles from the year 2016 to 2023. Moreover, the current survey article discusses the performance assessment employing different metrics and made a comparative assessment to determine the ML model’s effectiveness. From this survey, it is identified that Artificial Neural Network model has attained better results than other ML algorithms. Moreover, further ASD studies employing an ML strategy for feature selection, prediction and classification can greatly benefit from this research.

Graphical attention networks for autism spectrum disorder classification

Graphical attention networks for autism spectrum disorder classification

Enhancing Autism Spectrum Disorder Classification with Lightweight Quantized CNNs and Federated Learning on ABIDE-1 Dataset

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that presents significant diagnostic challenges due to its varied symptoms and nature. This study aims to improve ASD classification using advanced deep learning techniques applied to neuroimaging data. We developed an automated system leveraging the ABIDE-1 dataset and a novel lightweight quantized one-dimensional (1D) Convolutional Neural Network (Q-CNN) model to analyze fMRI data. Our approach employs the NIAK pipeline with multiple brain atlases and filtering methods. Initially, the Regions of Interest (ROIs) are converted into feature vectors using tangent space embedding to feed into the Q-CNN model. The proposed 1D-CNN is quantized through Quantize Aware Training (QAT). As the quantization method, int8 quantization is utilized, which makes it both robust and lightweight. We propose a federated learning (FL) framework to ensure data privacy, which allows decentralized training across different data centers without compromising local data security. Our findings indicate that the CC200 brain atlas, within the NIAK pipeline’s filt-global filtering methods, provides the best results for ASD classification. Notably, the ASD classification outcomes have achieved a significant test accuracy of 98% using the CC200 and filt-global filtering techniques. To the best of our knowledge, this performance surpasses previous studies in the field, highlighting a notable enhancement in ASD detection from fMRI data. Furthermore, the FL-based Q-CNN model demonstrated robust performance and high efficiency on a Raspberry Pi 4, underscoring its potential for real-world applications. We exhibit the efficacy of the Q-CNN model by comparing its inference time, power consumption, and storage requirements with those of the 1D-CNN, quantized CNN, and the proposed int8 Q-CNN models. This research has made several key contributions, including the development of a lightweight int8 Q-CNN model, the application of FL for data privacy, and the evaluation of the proposed model in real-world settings. By identifying optimal brain atlases and filtering methods, this study provides valuable insights for future research in the field of neurodevelopmental disorders.

Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder

Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder

Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2–30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

Personalized identification of autism-related bacteria in the gut microbiome using explainable artificial intelligence

Autism spectrum disorder (ASD) affects social interaction and communication. Emerging evidence links ASD to gut microbiome alterations, suggesting that microbial composition may play a role in the disorder. This study employs explainable artificial intelligence (XAI) to examine the contributions of individual microbial species to ASD. By using local explanation embeddings and unsupervised clustering, the research identifies distinct ASD subgroups, underscoring the disorder's heterogeneity. Specific microbial biomarkers associated with ASD are revealed, and the best classifiers achieved an AU-ROC of 0.965± 0.005 and an AU-PRC of 0.967± 0.008. The findings support the notion that gut microbiome composition varies significantly among individuals with ASD. This work's broader significance lies in its potential to inform personalized interventions, enhancing precision in ASD management and classification. These insights highlight the importance of individualized microbiome profiles for developing tailored therapeutic strategies for ASD.

A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.

Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT). Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%. This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

HYBRID OPTIMIZATION-ENABLED FUNCTIONAL CONNECTIVITY-BASED PIVOTAL REGION EXTRACTION AND TRANSFER LEARNING FOR AUTISM SPECTRUM DISORDER DETECTION

Autism Spectrum Disorder (ASD) is a neuro development-based disability caused by variations in the brain which occur during the early stages of human life. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. However, diagnosing ASD is difficult due to its complex symptoms as well as an inadequate number of neurobiological indications. This paper aims to detect ASD based on pivotal region extraction and Feedback-Henry Jellyfish Optimization-Convolution Neural Network with Transfer Learning (FHJO_CNN with TL) is developed in this work. Here, an autistic brain image is acquired from the data sample and it is fed to the pre-processing stage. By utilizing the Kalman filter and Region of Interest (RoI), selection pre-processing is done. After that, functional connectivity-based pivotal region extraction is performed based on the proposed FHJO, where the FHJO is an incorporation of Feedback-Henry Gas Optimization (FHGO) and Jelly Fish (JS) algorithms, where, FHGO is the combination of Henry Gas Solubility Optimization (HGSO), and Feedback Artificial Tree (FAT) algorithm. Thereafter in feature extraction, features like Local Binary Pattern (LBP), and Speeded Up Robust Features (SURF), statistical features are excerpted. Finally, ASD classification is done utilizing the proposed FHJO_CNN with TL. The performance of the proposed method is evaluated using the ACERTA-ABIDE dataset and the evaluation reveals that FHJO_CNN with TL attained values of accuracy, sensitivity and specificity which are 94.08%, 95.09%, and 93.59% respectively.

Social challenges, autism spectrum disorder, and attention deficit/hyperactivity disorder in youth with neurofibromatosis type I

Objective Youth with neurofibromatosis type I (NF1) demonstrate high rates of Autism Spectrum Disorder (ASD) and Attention Deficit/Hyperactivity Disorder (ADHD), which often have overlapping behaviors. Diagnostic clarity is important to guide services. This study evaluated ASD classification in NF1 using various methods and whether those with ADHD suspicion have more social challenges associated with ASD. Method 34 youth with NF1 (M age = 10.5 ± 1.6 years), completed ASD assessments that combined direct observation and informant ratings to yield a Clinician Best Estimate (CBE) classification. Caregivers rated ASD-related social challenges using the Social Responsiveness Scale- 2nd Edition (SRS-2). Results ASD classification varied depending on the method, ranging from 32% using low-threshold SRS-2 cut-scores (T ≥ 60) to under 6% when combining cut scores for diagnostic observational tools and stringent SRS-2 cut-scores (T ≥ 70). 14.7% had a CBE ASD classification. 44% were judged to have autism traits associated with a non-ASD diagnosis. The 52.9% with a suspicion of ADHD had higher SRS-2 scores than those without ADHD, F (7, 26) = 3.45, p < .05, Wilk’s lambda = 0.518, partial eta squared = 0.482. Conclusions Findings highlight the importance of rigorous diagnostic methodology when evaluating ASD in NF1 to inform the selection of targeted interventions for socialization challenges in NF1.

Autism spectrum disorder identification with multi-site functional magnetic resonance imaging

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by enduring difficulties in social interaction and communication. People analyzed with ASD may display repetitive behaviors and limited interests. Autism is classified as a spectrum disorder, implying that the symptom intensity might range from mild to severe depending on the individual. To detect ASD in this paper an attribute feature graph approach is designed by using the stastical dependencies features that necessarily accomplish the diagnosis of ASD. In the first phase the features extracted are designed based on the functional magnetic resonance imaging (fMRI) data, in the next-step the attribute feature graph layer learns the features of the node information of various nodes by ASD classification. Further, in the third step, it is employed to independently extract distinguishing features from the functional connectivity matrices of the brain that are derived from fMRI. The custom convolutional neural network (CNN) used in this study is trained on a comprehensive dataset comprising individuals diagnosed with ASD and typically developing individuals. In the fourth stage, a prototype learning is developed to augment the classification performance of the custom-CNN. The experimental analysis further carried out states that the proposed model works efficiently in comparison with the existing system.

Investigating multiclass autism spectrum disorder classification using machine learning techniques

The diagnosis and classification of autism spectrum disorder (ASD) presents anatomical difficulty owing to the existence of a wide range of symptoms that may be organized into many categories. The present research investigates the efficacy of machine learning methods for facilitating the recognition of individuals who have been diagnosed with ASD. The primary aim of this study has been to assess the effectiveness of multiple algorithms based on machine learning in identifying intricate patterns seen in datasets related to ASD, which includes a wide range of diagnostic categories. The results indicate that the Logistic Regression approach demonstrated great levels of accuracy, with rates of 94.3 % for children and 99 % for adolescents in the binary classification system. Similarly, it has been reported that the Support Vector Machine (SVM) had superior performance compared to all other systems in the binary classification test focused on adults exclusively, with an accuracy rate of 98.5 %. Moreover, a supplementary series of experiments conducted on the combined dataset of children, adolescents, and adults has resulted in the observation that Logistic Regression and SVM exhibited notable accuracy rates of 97.2 % for binary classification and 99.55 % for multiclass classification, encompassing individuals from diverse age groups. The results provide evidence in favor of that progress has been achieved in the diagnosis and treatment of ASD as a result of the capacity to detect and categorize the disorder at an earlier developmental phase.

Autism spectrum disorder identification using multi‐model deep ensemble classifier with transfer learning

AbstractIdentifying autism spectrum disorder (ASD) symptoms accurately is a challenging task. The traditional subjective diagnostic process of ASD relies on time‐consuming behavioural and psychological observations. In this study, we introduce an ensemble learning‐based classification model using an open‐access database focusing on functional magnetic resonance imaging (fMRI). We propose a novel multi‐model ensemble classifier (MMEC) and multisite ensemble classifier (MSEC) with transfer learning (TL) for ASD classification to improve the prediction accuracy. The MMEC utilizes four base classifiers, Inception V3, ResNet50, MobileNet, and DenseNet to boost the performance of the individual convolutional neural network (CNN) models. The MSEC combined the base classifiers trained from different data sites. We evaluate the two models with ensemble averaging, weighted averaging, and stacking methods. The proposed MMEC with stacking shows the state of art performance compared to MSEC, improving the prediction accuracy by 3.25%. The obtained results have shown an accuracy of 97.82%, 97.82%, and 97.78% for ensemble averaging, weighted averaging, and stacking methods, respectively, on multi‐site datasets. The ensemble classifier MMEC performed better than a single classifier on the multi‐site dataset. The proposed MMEC opens a new paradigm to design a universal ASD classification framework.

Classification of Autism Spectrum Disorder using Resting State-Functional Magnetic Resonance Imaging and Artificial Neural Network

Autism Spectrum Disorder (ASD) represents a growing challenge in public health, characterized by irreversible neurodevelopmental abnormalities. Despite a rising prevalence, the underlying aetiology and neural substrates of ASD remain incompletely understood. Resting state-functional Magnetic Resonance Imaging (rs-fMRI) has emerged as a valuable non-invasive tool for probing the organization and cognitive functions of the brain by capturing hemodynamic changes. This study explores the relationship between Functional Connectivity (FC) alterations, measured by rs-fMRI, and the manifestation of ASD-related brain disorders. In this paper, we propose a hybrid approach employing an InfoMax Independent Component Analysis (ICA) algorithm and Artificial Neural Network to distinguish between the ASD subjects and Healthy Controls (HCs). Initially, rs-fMRI datasets are preprocessed using Statistical Parametric Mapping (SPM). Further, these datasets are processed using InfoMax ICA to extract the distinct features such as fractional Amplitude of Low-Frequency Fluctuations (fALFF) and Dynamic Range and visualize the FC alterations by region mapping. In addition to this, the number of activated voxels for each brain regions are estimated to observe the neural abnormalities. Finally, the extracted features are fed as input to train, test and validate the ANN classifier. The efficacy of the proposed network is evaluated using the Autism Brain Imaging Data Exchange (ABIDE) dataset and an accuracy of 75.3 percentage is achieved.

Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

Diagnostic classification of autism spectrum disorder using sMRI improves with the morphological distance-related features compared to morphological features

Diagnostic classification of autism spectrum disorder using sMRI improves with the morphological distance-related features compared to morphological features

Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews

Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.