A multimodal machine learning system in early screening for toddlers with autism spectrum disorders based on the response to name

Abstract

BackgroundReduced or absence of the response to name (RTN) has been widely reported as an early specific indicator for autism spectrum disorder (ASD), while few studies have quantified the RTN of toddlers with ASD in an automatic way. The present study aims to apply a multimodal machine learning system (MMLS) in early screening for toddlers with ASD based on the RTN.MethodsA total of 125 toddlers were recruited, including ASD (n = 61), developmental delay (DD, n = 31), and typical developmental (TD, n = 33). Procedures of RTN were, respectively, performed by the evaluator and caregiver. Behavioral data were collected by eight-definition tripod-mounted cameras and coded by the MMLS. Response score, response time, and response duration time were accurately calculated to evaluate RTN.ResultsTotal accuracy of RTN scores rated by computers was 0.92. In both evaluator and caregiver procedures, toddlers with ASD had significant differences in response score, response time, and response duration time, compared to toddlers with DD and TD (all P-values < 0.05). The area under the curve (AUC) was 0.81 for the computer-rated results, and the AUC was 0.91 for the human-rated results. The accuracy in the identification of ASD based on the computer- and human-rated results was, respectively, 74.8 and 82.9%. There was a significant difference between the AUC of the human-rated results and computer-rated results (Z = 2.71, P-value = 0.007).ConclusionThe multimodal machine learning system can accurately quantify behaviors in RTN procedures and may effectively distinguish toddlers with ASD from the non-ASD group. This novel system may provide a low-cost approach to early screening and identifying toddlers with ASD. However, machine learning is not as accurate as a human observer, and the detection of a single symptom like RTN is not sufficient enough to detect ASD.

Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children

“…In other domains, such as brain imaging, combining different modalities has shown to represent a promising manner to increase classification accuracy [54][55][56] . A recent study that used acoustic analyses combined with a computer vision approach applied in the context of the paradigm of response to name has achieved 92% consistency with clinical ground-truth ratings 57 . Recently a large, multi-site study implemented a multi-modal, app-based approach to screening for autism signs that significantly outperformed the traditional parent-report screening questionnaires 58 .…”

Multimodal Deep Learning in Early Autism Detection—Recent Advances and Challenges

“…fMRI and sMRI play vital roles in accurate diagnosis [13]. AI-base both ML and DL approaches, but DL techniques are underutilized [14][15][16] in ASD diagnostics use DL models, combining neuroimaging methods w to identify early biological markers [17][18][19]. Lightweight CNN models racy, precision, and F1 score.…”

A new diagnostic autism spectrum disorder (DASD) strategy using ensemble diagnosis methodology based on blood tests

“…According to Zhu et al [ 40 ], a Response To Name (RTN) based on multimodal machine learning system was provided to accurately classify ASD disease using 125 toddlers where 61 of them are ASD, 31 of them are Developmental Delay (DD), and 33 of them are TD. Multimodal machine learning system has a significant impact on RTN where it can provide accurate results.…”