Novel AI driven approach to classify infant motor functions.
Cerebral Palsy
/ diagnosis
Child Development
/ physiology
Datasets as Topic
Female
Humans
Image Interpretation, Computer-Assisted
/ methods
Infant
Infant, Newborn
Longitudinal Studies
Machine Learning
Male
Mass Screening
/ methods
Movement
/ physiology
Pilot Projects
Prospective Studies
Video Recording
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
10 05 2021
10 05 2021
Historique:
received:
14
12
2020
accepted:
12
04
2021
entrez:
11
5
2021
pubmed:
12
5
2021
medline:
26
10
2021
Statut:
epublish
Résumé
The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA). This study proposes a novel machine learning algorithm to detect an age-specific movement pattern, the fidgety movements (FMs), in a prospectively collected sample of typically developing infants. Participants were recorded using a passive, single camera RGB video stream. The dataset of 2800 five-second snippets was annotated by two well-trained and experienced GMA assessors, with excellent inter- and intra-rater reliabilities. Using OpenPose, the infant full pose was recovered from the video stream in the form of a 25-points skeleton. This skeleton was used as input vector for a shallow multilayer neural network (SMNN). An ablation study was performed to justify the network's architecture and hyperparameters. We show for the first time that the SMNN is sufficient to discriminate fidgety from non-fidgety movements in a sample of age-specific typical movements with a classification accuracy of 88%. The computer-based solutions will complement original GMA to consistently perform accurate and efficient screening and diagnosis that may become universally accessible in daily clinical practice in the future.
Identifiants
pubmed: 33972661
doi: 10.1038/s41598-021-89347-5
pii: 10.1038/s41598-021-89347-5
pmc: PMC8110753
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
9888Références
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