Clinical trial NCT05456126: Artificial Intelligence for Infant Motor Screening: Development and Validation
Official title: Artificial Intelligence for Infant Motor Screening: Development and Validation
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Motor Disorders
Keywords: Artificial intelligence
Infant
Machine Learning
Motor Development
Screening
Lead sponsor: National Taiwan University Hospital
Sponsor class: OTHER
Collaborators: name: National Health Research Institutes, Taiwan
First posted: 2022-07-13
Enrollment: 125
Sex: ALL
Minimum age (years): 4
Maximum age (years): 18
Standard ages: CHILD
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: This study will prospectively recruit term and preterm infants from National Taiwan University Children's Hospital, Taipei, Taiwan. Infants will be recruited from the neonatal and well-baby follow-up clinic of the hospital. The proportion of boys and girls in the sample will be balanced. The parents will be informed of the study and will sign a written consent form before participation in the study. This study is currently under human right review by National Taiwan University Hospital.

Brief summary: The purpose of this three-year study is therefore three-fold: (1) Model Development- to apply pose estimation model and tracking recognition model on the movements of a large sample of term and preterm infants under a motor assessment in the laboratory to examine the accuracy of the AI algorithms in identifying individual movements using physical therapists' results as gold standards; (2) Model Validation- to examine the performance of the AI algorithms on the same term and preterm infants' movements when video recorded by the parents at home between the laboratory assessment ages using physical therapists' results as gold standards; and (3) Concurrent and Predictive Validity of AI Movement Sets- to select the identifiable movement classes into AI movement sets for individual ages to examine their concurrent validity with physical therapists' results and predictive validity on developmental outcomes at 18 months of age in these infants.

Detailed description: Background and Purpose. Although the number of children with developmental disorders reported for early intervention in Taiwan increases in the recent decade, the prevalence estimate of children with developmental disorders is lower than the global data particularly among those aged under 2 years or in remote areas. Artificial Intelligence (AI), based on machine learning of big data, has been successfully used for medical image classification and prediction in certain diseases; however, its application in child developmental screening is rare. The purpose of this three-year study is therefore three-fold: (1) Model Development- to apply pose estimation model and tracking recognition model on the movements of a large sample of term and preterm infants under a motor assessment in the laboratory to examine the accuracy of the AI algorithms in identifying individual movements using physical therapists' results as gold standards; (2) Model Validation- to examine the performance of the AI algorithms on the same term and preterm infants' movements when video recorded by the parents at home between the laboratory assessment ages using physical therapists' results as gold standards; and (3) Concurrent and Predictive Validity of AI Movement Sets- to select the identifiable movement classes into AI movement sets for individual ages to examine their concurrent validity with physical therapists' results and predictive validity on developmental outcomes at 18 months of age in these infants. Method. A total of 125 term and preterm infants will be recruited from National Taiwan University Children's Hospital and will be randomly split into the training (N=101), tuning (N=12), and testing sets (N=12) with 8:1:1 ratio for Model Development. All infants will be prospectively administered the Alberta Infant Motor Assessment in prone, supine, sitting and standing positions at 4, 6, 8, 10, 12 and 14 months of age (corrected for prematurity) in the laboratory with movements recorded by 5 cameras. For Model Validation, the same 125 infants will be video recorded their movements by the parents using cell phones at home at 5, 7, 9, 11 and 13 months of age from at least 2 camera views, with the movement records uploaded to a prototype of Mobile APP "Baby Go." The data processing of movement video records will include: selection of movement records, establishment of a pose estimation model, and establishment of an action recognition model. The accuracy of the AI model in identifying infants' individual movements will be examined using physical therapist's results as gold standards. The movements identifiable through machine learning will be selected to establish AI movement sets for each age. Concurrent and Predictive Validity of the AI movement sets will be respectively examined using physical therapist's results and developmental outcomes at 18 months of age as the criteria (age of walking attainment and the Peabody Developmental Motor Scale- 2nd edition). Significance. The results will help establish the best and appropriate AI model for infant motor screening in Taiwan. The established AI model may be incorporated into clinical procedure to assist pediatricians and physical therapists in planning for further diagnostic assessment.

Eligibility criteria: Inclusion Criteria:

* The inclusion criteria for term infants are: gestational age 37-42 weeks, birth weight \>2,500 grams, aged 2-4 months, and no congenital/genetic abnormalities.
* The inclusion criteria for preterm infants are: gestational age \<37 weeks, birth weight \<2,500 grams, aged 2-4 months (corrected for prematurity), and no congenital/genetic abnormalities.
* Their mothers are older than 20 years of age, have no history of alcohol or drug abuse, and are married or live with fathers.

Exclusion Criteria: No.

Primary outcomes:
measure: Alberta Infant Motor Scale (AIMS)
timeFrame: 4-18 months of age
measure: Age of walking attainment
timeFrame: 10-18 months of age
measure: Peabody Developmental Motor Scale- 2nd edition (PDMS-II)
timeFrame: 18 months of age
Secondary outcomes:
Other outcomes:
Locations:
city: Taipei
country: Taiwan
facility: National Taiwan University
Study officials:
affiliation: School and Graduate Institute of Physical Therapy, National Taiwan University
name: Suh-Fang Jeng, Professor
role: PRINCIPAL_INVESTIGATOR
References: