Deep learning platform for predicting autism diagnosis and subtypes
A Deep Learning Algorithm Platform to Predict Autism Diagnosis and Subtypes by Integrating Clinical, Cognitive, Imaging, Gut Microbiome, and Metabolome Data
This study is testing a new computer program that looks at gut bacteria and other information to see if it can help predict autism and its different types in kids with autism, their siblings, and typically developing children.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 420 (estimated) |
| Ages | 4 Years to 25 Years |
| Sex | All |
| Sponsor | National Taiwan University Hospital Academic / other |
| Locations | 1 site (Taipei) |
| Trial ID | NCT04873674 on ClinicalTrials.gov |
What this trial studies
This observational study aims to develop a deep learning algorithm to predict autism spectrum disorder (ASD) diagnoses and subtypes by analyzing the gut microbiome and other multi-dimensional data. It will involve a large-scale, prospective design that includes both ASD patients and their unaffected siblings, as well as typically developing controls. The study will collect comprehensive data on environmental factors, clinical assessments, neurocognitive measures, and metagenomics profiles to enhance understanding of ASD's pathogenetic mechanisms. The ultimate goal is to create a web application for clinical and academic use that improves early detection and treatment of ASD.
Who should consider this trial
Good fit: Ideal candidates include individuals aged 4 to 25 with a clinical diagnosis of ASD as defined by DSM-5 criteria, along with their unaffected siblings.
Not a fit: Patients with comorbid psychiatric or neurological disorders, or those with a first-degree relative who may have ASD, may not benefit from this study.
Why it matters
Potential benefit: If successful, this study could lead to improved early detection and personalized treatment strategies for autism spectrum disorder.
How similar studies have performed: While studies on the gut-brain axis in psychiatric disorders have shown promise, this specific approach utilizing deep learning for ASD diagnosis is relatively novel.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * ASD participants are (1) they have a clinical diagnosis of ASD defined by the DSM-5 criteria,1 made by board-certificated child psychiatrists and confirmed by the ADI-R/ADOS; (2) their ages range from 4 to 25; (3) both parents are Han Chinese; (4) they and their parents cooperate with all the assessments and stool and blood collection. Inclusion Criteria for US and TDC are (1) they do not reach the clinical diagnosis of ASD according to DSM-5 diagnostic criteria and the same criteria as described in the (2), (3), (4) and of Inclusion Criteria for ASD participants. Exclusion Criteria: * (1) comorbidity with DSM-5 diagnoses of schizophrenia, schizoaffective disorder, delusional disorder, other psychotic disorders, organic psychosis, schizotypal personality disorder, bipolar disorder, depression, severe anxiety disorders or substance use; (2) comorbidity with neurological or systemic disorders; and (3) having a first degree relative who may have ASD based on family history method assessment (the TDC group).
Where this trial is running
Taipei
- National Taiwan Univeristy Hospital — Taipei, Taiwan (Recruiting)
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.