Comutti - A Research Project Dedicated to Finding Smart Ways of Using Technology for a Better Tomorrow for Everyone, Everywhere. (COMUTTI)

IRCCS Eugenio Medea

Status: Completed

Conditions: Autism Spectrum Disorder

Treatments: Diagnostic Test: Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule; Behavioral: audio signal dataset creation and validation; machine learning analysis, empirical evaluations

Study type: Interventional

Funder types: Other

Identifiers

NCT05149144

868

Details and patient eligibility

About

According to World Health Organization, worldwide one in 160 children has an ASD. About around 25% to 30% of children are unable to use verbal language to communicate (non-verbal ASD) or are minimally verbal, i.e., use fewer than 10 words (mv-ASD). The ability to communicate is a crucial life skill, and difficulties with communication can have a range of negative consequences such as poorer quality of life and behavioural difficulties. Communication interventions generally aim to improve children's ability to communicate either through speech or by supplementing speech with other means (e.g., sign language, pictures, or AAC - Advanced Augmented Communication tools). Individuals with non- verbal ASD or mv-ASD often communicate with people through vocalizations that in some cases have a self-consistent phonetic association to concepts (e.g., "ba" to mean "bathroom") or are onomatopoeic expressions (e.g., "woof" to refer to a dog). In most cases vocalizations sound arbitrary; even if they vary in tone, pitch, and duration depending it is extremely difficult to interpret the intended message or the individual's emotional or physical state they would convey, creating a barrier between the persons with ASD and the rest of the world that originate stress and frustration. Only caregivers who have long term acquaintance with the subjects are able to decode such wordless sounds and assign them to unique meanings.

This project aims at defining algorithms, methods, and technologies to identify the communicative intent of vocal expressions generated by children with mv-ASD, and to create tools that help people who are not familiar with the subjects to understand these individuals during spontaneous conversations.

Enrollment: 33 patients

Sex: All

Ages: 2 to 10 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• having a clinical diagnosis of autism spectrum disorder according to DSM-5 criteria
• use fewer than 10 words

Exclusion criteria

• using any stimulant or non-stimulant medication affecting the central nervous system
• having an identified genetic disorder
• having vision or hearing problems
• suffering from chronic or acute medical illness

Trial design

Primary purpose: Basic Science

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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33 participants in 1 patient group

Experimental: audiosignal dataset creation and machine learning analysis

Experimental group

Description:

Experimental: audiosignal dataset creation and processing; machine learning analysis, empirical evaluations

Treatment:

Behavioral: audio signal dataset creation and validation; machine learning analysis, empirical evaluations

Diagnostic Test: Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule