Clinical trial NCT06633874: Smart Computing Models, Sensors, and Early Diagnostic Speech and Language Deficiencies Indicators in Child Communication
Official title: Smart Computing Models, Sensors, and Early Diagnostic Speech and Language Deficiencies Indicators in Child Communication
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Developmental Language Disorders
Developmental Articulation Disorder
Speech Disorder
Neurodevelopmental Disorder (Diagnosis)
Keywords: screening
developmental speech and language disorders
speech disorder
language disorder
developmental disorder
developmental deficiencies
neurodevelopmental disorder
Early Diagnostic Speech and Language Deficiencies Indicators
Machine Learning
Neural Networks
Serious Game
Child Communication
neurodevelopmental screening
Lead sponsor: University of Ioannina
Sponsor class: OTHER
Collaborators: name: DOTSOFT SA - GR
name: TELEGLOBAL LP - GR
name: Region of Epirus (Regional Operational Programme 'Epirus 2014-2020') - GR
name: University Hospital, Ioannina
First posted: 2024-10-09
Enrollment: 520
Sex: ALL
Minimum age (years): 4
Maximum age (years): 12
Standard ages: CHILD
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SCREENING

Brief summary: This clinical trial aims to justify a protocol for designing and developing an automated decision-making system to support and enhance screening and early detection procedures for developmental speech/language difficulties in child communication. The system will utilize smart computing models, sensors, and early diagnostic speech and language deficiency indicators. The study participants will be typically and non-typically (neurodevelopmentally atypical) developing children, primarily in preschool and elementary school.

The key research questions the study seeks to address are:

1. Do differences exist between typically developing and neurodevelopmentally atypical children when engaging with the serious game used in the study?
2. If such differences are identified, can the collected data be utilized to train an automated decision-making system capable of accurately distinguishing between typically and neurodevelopmentally atypical children based on speech and language deficiencies?

Researchers will compare typically and non-typically developed children to see if the system can incorporate multiple data points from assessment domains to create a diagnostic profile.

After the parents are informed of the study and provide written consent, they enroll in the system. Participants will be asked to wear a smartwatch and play a serious game on a tablet under the supervision of a clinician. The system will collect data from the gameplay and sensors.

Detailed description: In recent years, integrating digital and mobile technologies in health and education has opened new possibilities for monitoring and assessment. This study will build on these advancements by implementing artificial intelligence (AI) and machine learning models that offer real-time decision-making and personalized feedback. By automating critical aspects of the diagnostic process, this system will help bridge the gap between traditional clinical expertise and cutting-edge technology, ultimately enhancing early intervention efforts.

The SmartSpeech study will be a clinical trial designed to create and validate an automated decision-making system to enhance the screening and early detection of developmental speech and language difficulties in young children. This study will target typically and non-typically (neurodevelopmentally atypical) developing children, primarily focusing on preschool and elementary school-aged participants. The system will employ intelligent computing models, biometric sensors, and early diagnostic indicators of speech and language deficiencies to provide clinicians with a robust tool for identifying potential developmental issues.

Two primary research questions drive the trial:

1. Do differences exist between typically developing and neurodevelopmentally atypical children when engaging with the serious game used in the study?
2. If such differences are identified, can the collected data be utilized to train an automated decision-making system that accurately distinguishes between typically and neurodevelopmentally atypical children based on speech and language deficiencies?

To assess speech and language developmental skills and their manifestations, the interdisciplinary team will design a serious game (SG) based on theories that measure speech and language and psychomotor, cognitive, psychoemotional, and hearing skills. Each domain will assess the performance of specific tasks within the SG activities for the participant.

To address the questions of this study, data from typically developing children will be compared with data from non-typically developing children, analyzing whether the AI system can synthesize multiple data points across assessment domains to generate a comprehensive diagnostic profile.

Participants will be recruited through an open call distributed via private and public health and educational establishments across Greece. The recruitment process will focus on parents of typically and non-typically developing children aged 4 to 12. Parents will be invited to attend informational meetings where the objectives, procedures, and ethical guidelines of the study will be thoroughly explained.

During these sessions, parents will receive comprehensive information about the purpose of the study, including the specific role of the serious game developed in the study. The nature of the game, the expected involvement of their children, and the data collection methods will be discussed in detail to ensure transparency. Parents will be asked to provide informed written consent for their children to participate in full compliance with the General Data Protection Regulation and ethical considerations. These steps are essential to ensure that the rights and privacy of the participants will be protected throughout the study. Parents will also provide their child's developmental history in an online questionnaire embedded in the system.

The study will include typically developing preschool and school-aged children and non-typically developing children. It will not include children with other medical conditions or on medications that could potentially influence the results.

After an informative meeting and written consent from parents, participants will wear a smartwatch and engage in a serious game (designed and developed by the research team). Children will play the SG under the direct supervision of clinicians. This game will be designed to assess various developmental domains, including speech, language, psychomotor skills, cognitive function, psychoemotional behavior, and hearing abilities. The game will capture speech and language data through gameplay interactions alongside sensor data from the wearable device and the tablet camera (eye tracking and heart rate variability).

This game-based approach will naturally and engagingly collect diverse data points, ensuring a comprehensive evaluation of the child\&amp;#39;s speech and language abilities.

The system will form its datasets for analysis to detect speech and language development, feeding data into the selected artificial intelligence algorithms. The study will measure communication, psychomotor, cognitive, and psychoemotional skills through designed activities. The responses during gameplay will be evaluated, creating the developmental profile of the participant.

This structured and ethical recruitment process will ensure complete adherence to data protection and ethical standards while maintaining active engagement from both parents and children. By incorporating face-to-face supervision and real-time monitoring during gameplay, the study will guarantee the quality and integrity of the data collected for analysis.

The SG, paired with sensors from the smartwatch, will collect real-time data, including:

* Game responses
* Speech responses
* Gaze data from the tablet camera
* Heart Rate Variability (captured through the smartwatch)

This multi-modal data will be processed using neural network/machine learning algorithms to classify speech and language abilities across different developmental stages. The use of these advanced AI models will enable the system to:

* Identify patterns of speech/language deficiencies.
* Differentiate between typically and non-typically developed children.
* Create individualized diagnostic profiles for each participant.

Finally, an evaluation and verification of the intelligent system will be performed.

Eligibility criteria: Inclusion Criteria:

* Typically developing preschoolers and school-aged children
* Children diagnosed with neurodevelopmental disorders
* Children should be native Greek speakers

Exclusion Criteria:

* Children with other medical conditions
* Children on medications that might affect the results of the study

Primary outcomes:
measure: Speech and language for TD children
timeFrame: 10-20 minutes
measure: Psychomotor for TD children
timeFrame: 10-20 minutes
measure: Cognitive for TD children
timeFrame: 10-20 minutes
measure: Psychoemotional for TD children
timeFrame: 10-20 minutes
measure: Hearing for TD children
timeFrame: 10-20 minutes
measure: Eye-tracking fixation duration for TD children
timeFrame: 1-10 minutes
measure: Eye-tracking trajectory path for TD children
timeFrame: 1-10 minutes
measure: Heart rate variability for TD children
timeFrame: 1-10 minutes
measure: Speech and language for non-TD children
timeFrame: 10-20 minutes
measure: Psychomotor for non-TD children
timeFrame: 10-20 minutes
measure: Cognitive for non-TD children
timeFrame: 10-20 minutes
measure: Psychoemotional for non-TD children
timeFrame: 10-20 minutes
measure: Hearing for non-TD children
timeFrame: 10-20 minutes
measure: Eye-tracking fixation duration for non-TD children
timeFrame: 1-10 minutes
measure: Eye-tracking trajectory path for non-TD children
timeFrame: 1-10 minutes
measure: Heart rate variability for non-TD children
timeFrame: 1-10 minutes
Secondary outcomes:
Other outcomes:
Locations:
city: Ioannina
country: Greece
facility: University of Ioannina
Study officials:
affiliation: University of Ioannina, Greece
name: Eugenia I Toki, PhD
role: STUDY_CHAIR
References: