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

NCT ID: NCT05149144

Last Updated: 2025-05-13

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 33 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2021-07-27
• Study Completion Date: 2024-12-31

Brief Summary

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.

Conditions

Autism Spectrum Disorder

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: BASIC_SCIENCE
• Blinding Strategy: NONE

Study Groups

Experimental: audiosignal dataset creation and machine learning analysis

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

Group Type: EXPERIMENTAL

Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule

Intervention Type: DIAGNOSTIC_TEST

Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule

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

Intervention Type: BEHAVIORAL

The project tests and adapts the technology developed at MIT for vocalization collection and labeling, and contributes to data gathering among Italian subjects (and their quality validation) in order to create a multi-cultural dataset and to enable cross-cultural studies and analyses. Next, the focus is placed on the analysis of harmonic features of the audio in the vocalizations of the dataset to identify recurring individual features and patterns corresponding to specific communications purposes or emotional states. Supervised and unsupervised machine learning approaches are developed and different machine learning algorithms will be compared to identify the most accurate ones for the project goal. Last, an exploratory evaluation of the vocalization-understanding machine learning model is conducted to test the usability and utility of the tool for vocalization interpretation.

Interventions

Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule

Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule

Intervention Type: DIAGNOSTIC_TEST

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

The project tests and adapts the technology developed at MIT for vocalization collection and labeling, and contributes to data gathering among Italian subjects (and their quality validation) in order to create a multi-cultural dataset and to enable cross-cultural studies and analyses. Next, the focus is placed on the analysis of harmonic features of the audio in the vocalizations of the dataset to identify recurring individual features and patterns corresponding to specific communications purposes or emotional states. Supervised and unsupervised machine learning approaches are developed and different machine learning algorithms will be compared to identify the most accurate ones for the project goal. Last, an exploratory evaluation of the vocalization-understanding machine learning model is conducted to test the usability and utility of the tool for vocalization interpretation.

Intervention Type: BEHAVIORAL

Eligibility Criteria

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

• Minimum Eligible Age: 2 Years
• Maximum Eligible Age: 10 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Politecnico di Milano

OTHER

Sponsor Role: collaborator

Massachusetts Institute of Technology

OTHER

Sponsor Role: collaborator

IRCCS Eugenio Medea

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Alessandro Crippa, Ph.D.

Role: PRINCIPAL_INVESTIGATOR

IRCCS Eugenio Medea

Locations

Scientific Institute, IRCCS Eugenio Medea

Bosisio Parini, Lecco, Italy

Countries

Italy

Other Identifiers

868

Identifier Type: -

Identifier Source: org_study_id