Autism Spectrum DisorderCOMUTTINCT05149144
Comutti - A Research Project Dedicated to Finding Smart Ways of Using Technology for a Better Tomorrow for Everyone, Everywhere.
A study testing Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule and audio signal dataset creation and validation; machine learning analysis, empirical evaluations in people with Autism Spectrum Disorder, run by IRCCS Eugenio Medea at 1 site in Italy.
Trial 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). Read more about this study
- Who is it for?
- Ages 2 to 10 years
- Conditions the study is for
- Autism Spectrum Disorder
- What is being tested?
- Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule and audio signal dataset creation and validation; machine learning analysis, empirical evaluations
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 33 participants
- Where
- 1 siteItaly
- Study dates
- 27 Jul 2021to 31 Dec 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 2 to 10 years
- All sexes
- No healthy volunteers
- Child
Who may be able to join
- having a clinical diagnosis of autism spectrum disorder according to DSM-5 criteria
- use fewer than 10 words
Who cannot join
- 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 locations and contacts
1 site in 1 country.
Italy1 site
Scientific Institute, IRCCS Eugenio Medea
Bosisio Parini, Lecco, 23842
About this study
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
- Being tested
- Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule, audio signal dataset creation and validation; machine learning analysis, empirical evaluations
- Keywords
- non-verbal autistic children, minimally-verbal autistic children
- Medical subject headings
- Autism Spectrum Disorder
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Clinical evaluation of participants by means of Autism Diagnostic Observation ScheduleDiagnostic test
Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule
audio signal dataset creation and validation; machine learning analysis, empirical evaluationsBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| Experimental: audiosignal dataset creation and machine learning analysis | Experimental | Experimental: audiosignal dataset creation and processing; machine learning analysis, empirical evaluations Receives: 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
Frequency of audio signal samples and their associated labels
Frequency (measured in number per hour) of audio signal samples (sounds and verbalizations) produced by each participant recorded during the hospital stays, in various contexts (i.e., during educational interventions and / or in moments of unstructured play) labeled as self-talk, delight, dysregulation, frustration, request, or social exchange.
A small, wireless recorder (Sony TX800 Digital Voice Recorder TX Series) will be attached to the participant's clothing using strong magnets. Next, the adults (caregiver and / or operators) must associate the sounds produced by the child to an affective and / or to the probable meaning of the vocalization -labels- through the use of a web app.
Measured: immediately after the intervention
Participant-specific harmonic features derived by the audio signal samples
Temporal and spectral audio features -i.e., pitch-related features, formants features, energy-related features, timing features, articulation features- extracted from the samples and used next for supervised and unsupervised machine learning analysis.
The collected audio signal samples will be segmented in the proximity of the temporal locations of labels. Next, it will be segmented and associated with temporally adjacent labels (affective states or probable meaning of vocalizations). Audio harmonic features (temporal/phonetic characteristics) will be then identified for each participant using supervised/unsupervised machine learning analysis of audio signal samples. Through this process, participant-specific patterns corresponding to specific communications purposes or emotional states will be identified.
Measured: immediately after the intervention
Accuracy of machine learning prediction
The classification accuracy of machine learning analysis, i.e., the number of correct predictions divided by the total number of predictions, which will be tested in a retained test set of recorded audio signal samples.
This outcome measures will estimate the usability/utility of the developed tool for vocalization interpretion based on a machine learning analysis of the recorded audio signal samples.
Measured: immediately after the intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Dec 2021
- Study start27 Jul 2021
- Primary completion31 Dec 2024
- Study completion31 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Autism Spectrum DisorderStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 33 participants (actual)
Sponsor and investigators
- Lead sponsor
- IRCCS Eugenio Medea
- Sponsor type
- University, hospital or other
- Collaborators
- Politecnico di Milano, Massachusetts Institute of Technology
- Responsible party
- Sponsor
- Investigators
- Alessandro Crippa, Ph.D., Principal investigator, IRCCS Eugenio Medea
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05149144
- Sponsor's protocol ID
- 868
- Acronym
- COMUTTI
- Status last verified
- May 2025
- First submitted
- 25 Nov 2021
- Last updated
- 13 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comutti - A Research Project Dedicated to Finding Smart Ways of Using Technology for a Better Tomorrow for Everyone, Everywhere.
- ClinicalTrials.gov ID
- NCT05149144
- Acronym / sponsor ID
- COMUTTI / 868
- Conditions
- Autism Spectrum Disorder
- Keywords
- non-verbal autistic children, minimally-verbal autistic children
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 33 participants (actual)
- Lead sponsor
- IRCCS Eugenio MedeaUniversity, hospital or other
- Collaborators
- Politecnico di Milano, Massachusetts Institute of Technology
- Study start
- 27 Jul 2021 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 31 Dec 2024 (actual)
- First posted
- 8 Dec 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Experimental: audiosignal dataset creation and machine learning analysis | Experimental | Diagnostic Test: Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule, Behavioral: audio signal dataset creation and validation; machine learning analysis, empirical evaluationsExperimental: audiosignal dataset creation and processing; machine learning analysis, empirical evaluations |
Who may qualify
- Ages
- 2 to 10 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
Key inclusion criteria (2 listed)
- having a clinical diagnosis of autism spectrum disorder according to DSM-5 criteria
- use fewer than 10 words
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Clinical evaluation of participants by means of Autism Diagnostic Observation ScheduleClinical evaluation of participants by means of Autism Diagnostic Observation Schedule | Diagnostic test | Experimental: audiosignal dataset creation and machine learning analysis |
| audio signal dataset creation and validation; machine learning analysis, empirical evaluationsThe 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. | Behavioural | Experimental: audiosignal dataset creation and machine learning analysis |
Primary outcome
Frequency of audio signal samples and their associated labels
Time frame: immediately after the intervention
Frequency (measured in number per hour) of audio signal samples (sounds and verbalizations) produced by each participant recorded during the hospital stays, in various contexts (i.e., during educational interventions and / or in moments of unstructured play) labeled as self-talk, delight, dysregulation, frustration, request, or social exchange. A small, wireless recorder (Sony TX800 Digital Voice Recorder TX Series) will be attached to the participant's clothing using strong magnets. Next, the adults (caregiver and / or operators) must associate the sounds produced by the child to an affective and / or to the probable meaning of the vocalization -labels- through the use of a web app.
Participant-specific harmonic features derived by the audio signal samples
Time frame: immediately after the intervention
Temporal and spectral audio features -i.e., pitch-related features, formants features, energy-related features, timing features, articulation features- extracted from the samples and used next for supervised and unsupervised machine learning analysis. The collected audio signal samples will be segmented in the proximity of the temporal locations of labels. Next, it will be segmented and associated with temporally adjacent labels (affective states or probable meaning of vocalizations). Audio harmonic features (temporal/phonetic characteristics) will be then identified for each participant using supervised/unsupervised machine learning analysis of audio signal samples. Through this process, participant-specific patterns corresponding to specific communications purposes or emotional states will be identified.
Accuracy of machine learning prediction
Time frame: immediately after the intervention
The classification accuracy of machine learning analysis, i.e., the number of correct predictions divided by the total number of predictions, which will be tested in a retained test set of recorded audio signal samples. This outcome measures will estimate the usability/utility of the developed tool for vocalization interpretion based on a machine learning analysis of the recorded audio signal samples.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 May 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Alessandro Crippa, Ph.D. principal investigator
IRCCS Eugenio Medea
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Clinical evaluation of participants by means of Autism Diagnostic Observation Schedule and audio signal dataset creation and validation; machine learning analysis, empirical evaluations in people with Autism Spectrum Disorder, run by IRCCS Eugenio Medea at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Frequency of audio signal samples and their associated labels (time frame: immediately after the intervention). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 May 2025.