Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT05456126 |
| Other study ID # |
202012089RINB |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
August 1, 2021 |
| Est. completion date |
June 1, 2025 |
Study information
| Verified date |
May 2024 |
| Source |
National Taiwan University Hospital |
| Contact |
Suh-Fang Jeng, Professor |
| Phone |
886-2-33668132 |
| Email |
jeng[@]ntu.edu.tw |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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.
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.
