Motor Disorders
Conditions
Keywords
AI, infant motor assessment, APP, validation
Brief summary
The purpose of this study is therefore five-fold: (1) designation of an APP Baby Go version 3.0 to include the assessment, follow-up, and education functions for parental use at home, (2) development and validation of the AI algorithm for infant motor assessment based on home videos obtained from term and preterm infants, (3) comparison of parental perception and report with AI-driven assessment results, (4) examination of the predictive validity of the AI algorithm for infant motor assessment on subsequent outcome, and (5) investigation of the usability of the APP Baby Go version 3.0 in parents and clinicians.
Detailed description
Background and Purpose: Early identification and intervention of infants who are at risk of developmental disorders (such as preterm infants) is an important global health policy and action. The number of children with developmental disorders referred for early intervention in Taiwan has increased in the last ten years. Yet, they are more likely diagnosed and referred for intervention at an age beyond two years. Existing developmental diagnostic tests are frequently accessible at hospitals, whereas screening tests are often based on parental reports that are influenced by parents' knowledge and interpretation. Although the emerging artificial intelligence (AI) technology and deep learning have enabled the tracking and recognition of human movements in standardized laboratory settings, whether its incorporation with mobile application (APP) is feasible and accurate for infant motor assessment at home has rarely been investigated. Therefore, this study continues our previous endeavors that applied AI and machine learning to classify several infant movements at standardized laboratory. This study aims to combine the AI algorithm and machine learning with an APP for infant motor assessment in home setup. The specific purposes are (1) designation of an APP Baby Go version 3.0 to include the assessment, follow-up, and education functions for parental use at home, (2) development and validation of the AI algorithm for infant motor assessment based on home videos obtained from term and preterm infants, (3) comparison of parental perception and report with AI-driven assessment results, (4) examination of the predictive validity of the AI algorithm for infant motor assessment on subsequent outcome, and (5) investigation of the usability of the APP Baby Go version 3.0 in parents and clinicians. Method: This study will recruit 100 preterm infants, 20 term infants aged 2 to 18 months (corrected for prematurity), 120 infants' parents, and 2 clinicians at National Taiwan University Children's Hospital. The APP Baby Go version 3.0 will contain the features of age-based motor assessment with 2 to 5 movements at each age, follow-up, and education module. The parents will be asked to video record their baby's movements in prone, supine, sitting, and standing at home biweekly and to simultaneously upload the video files via the APP during the age period of 2 to 18 months, followed by recording their infant's age of walking attainment. Trained physiotherapists will annotate all video files and the results will serve as the gold standards for validation of the data of the AI model and parental perception. The video data will be randomly split into the training and testing set with an 8:2 ratio for model development and validation. The AI model of infant motor assessment will be examined for its predictive validity on age of walking attainment. The parents and clinicians will fill out the APP usability survey. Innovation and Significance: This study is an incremental AI model advancement in tracking and recognizing infant movements from a laboratory-based classification system to a home-based assessment system. The automatic AI-driven infant motor assessment via the APP Baby Go will provide parents and healthcare providers in Taiwan with innovative and feasible developmental resources in remote communities. The results are insightful to assist pediatricians and physiotherapists in planning diagnostic assessment and early intervention for infants at risk of neuromotor disorders.
Interventions
None listed
Sponsors
National Taiwan University Hospital
National Health Research Institutes, Taiwan
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
2 Months to 18 Months
Healthy volunteers
Yes
Inclusion criteria
The inclusion criterion is: Preterm infants: gestational age \< 37 weeks, birth body weight \< 2,500 grams, and corrected age of 2-18 months. Term infants: gestational age 37-42 weeks, birth body weight \>2,500 grams, and age of 2-18 months. Clinicians: who provide early intervention to the participating infants in this study. The exclusion criterion is: Infants: parents can not read Chinese. Clinicians: can not read Chinese
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Alberta Infant Motor Scale (AIMS) | 3 - 18 months of age | motor function in supine, prone, sitting and standing position |
| Age of walking attainment | 9 - 18 months of age | Age of attaining independent walking for at least five steps |
| User experience from parents | When the infant approached 6 months, 12 months, and 18 months | Parents' perspectives on the use of the APP Baby Go were collected using Google Forms (For parents: https://forms.gle/BPL7CWopaB7qk3388). The questionnaire for parents was customized for the APP Baby Go in this study and adapted from a survey used in the Baby Moves APP previously described by Kwong et al. The questionnaire for the parents consisted of three sections: (1) frequency of the APP use, (2) benefits of the APP use, and (3) user's experience. Questionnaires contained three types of measures: (1) multiple choice, (2) five-point Likert scale, and (3) open-ended questions. |
| User experience from clinicians | When the infant 18 months | Clinicians' perspectives on the use of the APP Baby Go were collected using Google Forms (for clinicians: https://forms.gle/3L7nGKe2ZTT39t7n6). The questionnaire for clinicians is customized for the APP Baby Go in this study and adapted from a healthcare professional interview previously described by AlMahadin et al. 44 The questionnaire for clinicians contains two sections: (1) perspectives of the APP use and (2) recommendations of APP use. Each questionnaire contains three types of measures: (1) multiple choice, (2) five-point Likert scale, and (3) open-ended questions. |
Countries
Taiwan
Contacts
Primary ContactSuh-Fang Jeng, Professor
Outcome results
None listed