ArticleJournal of autism and developmental disorders2022
A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT).
Article in Journal of autism and developmental disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.Autism research : official journal of the International Society for Autism Research · 2026Pooled it
- Early Autism Spectrum Disorder Detection Using Adaptive Fused Spatial-Temporal Graph Convolutional Network Optimized With Artificial Lemming Algorithm.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Article
- An End-to-End Deep Learning System for Gastrointestinal Bleeding Detection and Quantification in Wireless Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting Autism Spectrum Disorder in Children Using Glowworm Optimization With Extreme Learning Machine Networks.Brain and behavior · 2026Article
- Deep neural networks and deep deterministic policy gradient for early ASD diagnosis and personalized intervention in children.Scientific reports · 2025Article
- Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10.Scientific reports · 2025Article
- Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model.Frontiers in artificial intelligence · 2025Article
- Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening.Health information science and systems · 2024Article
- ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis.Scientific reports · 2024Article
- Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification.PloS one · 2024Article
- Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2022Article
- Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.