The combined model demonstrated high diagnostic effectiveness, achieving an accuracy of 80%, precision of 82%, sensitivity of 73%, and specificity of 87%. Notably, our analysis revealed that interview-based texts were more diagnostically effective than story-recounting texts. ... Durrleman S, Delage H. Autism spectrum disorder and specific ...

Read the full text for the diagnostic criteria of autism spectrum disorder (ASD) from the Diagnostic and Statistical Manual of Mental Disorders (DSM-5).

May 16, 2024 · To meet diagnostic criteria for ASD according to DSM-5, a child must have persistent deficits in each of three areas of social communication and interaction (see A.1. through A.3. below) plus at least two of four types of restricted, repetitive behaviors (see …

May 1, 2024 · Although ASD can be diagnosed as early as 15 to 18 months of age, the average age of diagnosis is about 4.5 years, and some people are not diagnosed until adulthood. That’s unfortunate, as prompt diagnosis is important for early intervention.

Aug 14, 2023 · Through this paper, a new Diagnostic Autism Spectrum Disorder (DASD) strategy is presented to quickly and accurately detect ASD children. DASD contains two layers called Data Filter Layer (DFL) and Diagnostic Layer (DL).

This article intends to combine the behavioral observations and socio-demographic data of children with ASD, apply ML methods to the diagnosis of ID in autistic children, and optimize the diagnostic model through feature selection.

Information about the diagnostic criteria used for an autism diagnosis and the tools used by the specialist team carrying out the assessment.

When a child’s symptoms clearly indicate ASD, an experienced or trained sole paediatric care provider can independently diagnose ASD, based on clinical judgement and DSM-5 criteria (17), with or without data obtained using a diagnostic assessment tool (Table 1).

Aug 20, 2021 · The two main tools used in the diagnosis of ASD are DSM-5 and M-CHAT, which examine persistent deficits in interaction and social communication, and analyze responses to “yes/no” items that cover different developmental domains to formulate a diagnosis.

Jan 1, 2025 · Mingzhi Wang et al., (2023) developed a WL-DeepGCN framework that combines fMRI data and non-imaging demographic information for ASD diagnosis. The model uses a weight-learning network to define graph edge weights in the latent space, and residual connections in the GCN to avoid gradient issues.