A Preliminary Study on Artificial Intelligence for Infant Motor Screening
Condition: Motor Disorders
MeSH terms: Motor Disorders
Study type: Observational [Patient Registry]

Summary: 
      The purpose of this preliminary study is to apply AI technology on a sample of infants aged 4
      to 18 months to develop an action tracking and recognition algorithm for infant motor
      screening and to determine the accuracy of the captured movements during the Alberta Infant
      Motor Scale (AIMS) assessment using an experienced physical therapists' assessment results as
      the reference.
    

Detailed description: 
      Background and Purpose. Although there is an increase in the public awareness of early
      intervention for children with developmental disorder in Taiwan, the number of children
      reported for early intervention by the Ministry of Health and Welfare is limited,
      particularly for those aged under two years, outside of hospital settings, and in remote
      areas. This has highlighted the need of early screening for infants who are at risk or have
      developmental disorders. While motor development has a potential impact on the emergence of
      abilities in other domains in children at later age, motor screening may serve as the
      cornerstone to help detect signs of developmental dysfunction. Artificial Intelligence (AI),
      based on machine learning of big data, may be an alternative for assisting healthcare
      professionals to efficiently screen children's development and to help plan for further
      diagnostic assessment. The purpose of this preliminary study is to apply AI technology on a
      sample of infants aged 4 to 18 months to develop an action tracking and recognition algorithm
      for infant motor screening and to determine the accuracy of the captured movements during the
      Alberta Infant Motor Scale (AIMS) assessment using an experienced physical therapists'
      assessment results as the reference. Method. This study will recruit 50 infants (40 preterm
      infants and 10 term infants age 4-18 months (corrected age for preterm infants) from the
      National Taiwan University Children's Hospital. Each infant will be evaluated by a physical
      therapist for their gross motor development during prone, spine, sitting and standing
      positions using the AIMS assessment. The whole assessment procedure will be video recorded by
      five cameras. The data processing of movement video records will consist of selection of
      movement records, establishment of a pose estimation model, and establishment of an action
      recognition model. The accuracy of the pose and action recognition model in identifying
      infants' movements will be examined using the physical therapist's results as the gold
      standard. The results of this study will provide preliminary data to help establish the best
      and appropriate action recognition model of infant motor screening for future validation on a
      large sample.
    

Observational model: Cohort
Time perspective: Prospective
Sampling method: Probability Sample
Study population: 
        The inclusion criteria for preterm infants are: gestational age <37 weeks, birth weight
        <2,500 grams, 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.
      
Keywords: AI; Infant; Motor; Screening