Ignite Creation Date:
2024-10-26 @ 3:36 PM
Last Modification Date:
2024-10-26 @ 3:36 PM
Study NCT ID:
NCT06521918
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-07-21
Brief Title:
Combined Artificial Intelligence and Mobile Application for Remote Infant Motor Screening Development and Validation
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Combined Artificial Intelligence and Mobile Application for Remote Infant Motor Screening Development and Validation
Status:
RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The purpose of this three-year study is therefore four-fold 1 designation of an APP Baby Go version 30 to include the assessment referral and education functions for parental use at home 2 development and validation of the AI algorithm for infant motor screening based on home videos obtained from term and preterm infants 3 comparison of parental perception and report with AI-driven assessment results and 4 examination of predictive validity of the AI algorithm for infant motor screening on subsequent outcome
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 recent ten years and yet they are more likely diagnosed and referred for intervention at age beyond two years Existing developmental diagnostic tests are frequently accessible at hospitals whereas screening tests are often based on parental report that is 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 screening at home has rarely been investigated Therefore this study continues our previous endeavors that applied the 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 screening in home setup The specific purposes are 1 designation of an APP Baby Go version 30 to include the assessment referral and education functions for parental use at home 2 development and validation of the AI algorithm for infant motor screening based on home videos obtained from term and preterm infants 3 comparison of parental perception and report with AI-driven assessment results and 4 examination of predictive validity of the AI algorithm for infant motor screening on subsequent outcome Method This study will recruit 100 preterm infants and 20 term infants aged 2 to 18 months corrected for prematurity at National Taiwan University Childrens Hospital The APP Baby Go version 30 will contain the features of age-based motor screening with 2 to 5 movements at each age doctoral referral and education module The parents will be asked to video record their babys 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 infants age of walking attainment All video files will be annotated by trained physiotherapists that the results will serve as the gold standards for validation of the data of AI model and parental perception The video data will be randomly split into the training and testing set with 82 ratio for model development and validation The AI model of infant motor screening will be examined for its predictive validity on age of walking attainment Innovation and Significance This study is an incremental AI model advancement in tracking and recognition of infant movements from a laboratory-based classification system to a home-based screening system The automatic AI-driven infant motor screening via the APP Baby Go will provide parents and healthcare providers in Taiwan 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None