Artificial intelligence identifies key patterns from video footage of infant movements

March 26, 2019

Subtle characteristics in the spontaneous movement of very young babies may reveal clinically important aspects of their neurodevelopment. Visual assessment of typical movement patterns (General movements, GM) by a clinical expert is known to be effective in early identification of e.g. cerebral palsy (CP).

"A three month old infant shows frequently occurring stereotypical, dancing-like movements throughout the body and limbs. A noted absence of them is highly predictive of later emergence of CP," says Sampsa Vanhatalo, professor of clinical neurophysiology, University of Helsinki.

A very early identification and subsequent therapeutic intervention would be highly beneficial for alleviating the neurodevelopmental impact of CP. Currently, a child is diagnosed with CP at much later age, typically between 6 months and 2 years of age. GM analysis holds promise in early detection of CP, however, it needs special expertise that is currently obtained through international teaching courses, which effectively limits the number of doctors or therapists with the relevant skills. In addition, GM analysis in its present form is based on visual assessment, which is always subjective.

"There is an urgent need for objective and automated methods. They would allow employing movement analyses at much wider scale, and make it accessible to basically most, if not all, children in the world," says Vanhatalo.


Researchers at University of Helsinki and University of Pisa set out to explore the possibility that a conventional video recording of an infant lying in bed could be transformed to a quantified analysis of infant movements. They collaborated with people from an AI company based in Tampere, Neuro Event Labs, who were able to create a method for an accurate extraction of children's movements (using a technique known as pose estimation), allowing for the construction of a simplified "stick man" (or skeleton) video.

Next, the researchers gave the the stick figure videos to doctors with GM expertise to see whether diagnostically crucial information was preserved in those videos.

Using the stick figure videos alone, the doctors were able to assign diagnostic groups in 95% of cases, proving that the clinically essential information had been preserved.

The study shows that an automated algorithm may extract clinically important movement patterns from normal video recordings. These stick figure extractions can be directly used for quantitative analyses.

To demonstrate such potential, the researchers provided a proof of concept analysis where simple measures of stick figure movements showed clear differences between groups of infants with either normal or abnormal movements.

Use of stick figure videos also enables world-wide sharing among research communities without privacy concerns. This has been a significant bottleneck in setting up multinational research activities within this domain.

"This will finally enable a genuinely Big Data kind of development for better quantitative movement analyses in infants," Vanhatalo states.

"Since this study, we have collected larger datasets, including 3D video recordings, and we are currently developing an AI-based method for infantile motor maturity assessment. The rationale is straightforward: there is a developmental issue with the child, if the computational assessment of the motor maturity does not match with the child's true age."


In addition to early CP detection, automated movement analyses have many potential applications in the assessment of infant neurological development.

"We could create one kind of functional growth chart," says Vanhatalo.

Movement analyses could also be used in diverse ways to improve therapeutic decisions. Such methods could provide quantitative means to objectively measure efficacy of different therapeutic strategies; one of the global hot topics in restorative medicine.

Automated movement analyses could also allow out-of-hospital screening of children to identify those that need further care, or to provide assurance of normality in cases with concern about child's development.

"Use of machine learning and artificial intelligence allows for the extraction of substantial amounts of clinically useful information from a simple home-grade video recording. The ultimate aim is to find methods that will make it possible to provide high and even quality infant healthcare everywhere in the world," Vanhatalo summarizes.

The study was a collaboration between researchers from University of Helsinki, Helsinki University Hospital, University of Pisa, Scuola Superiore San'Anna ja IRCCS Stella Maris Foundation from Pisa, Istituto Superiore di Sanità from Rome, and Neuro Event Labs Ltd from Tampere. The study was supported by Arvo and Lea Ylppö Säätiö, Finnish Pediatric Foundation, and Finnish Academy.

University of Helsinki

Related Artificial Intelligence Articles from Brightsurf:

Physics can assist with key challenges in artificial intelligence
Two challenges in the field of artificial intelligence have been solved by adopting a physical concept introduced a century ago to describe the formation of a magnet during a process of iron bulk cooling.

A survey on artificial intelligence in chest imaging of COVID-19
Announcing a new article publication for BIO Integration journal. In this review article the authors consider the application of artificial intelligence imaging analysis methods for COVID-19 clinical diagnosis.

Using artificial intelligence can improve pregnant women's health
Disorders such as congenital heart birth defects or macrosomia, gestational diabetes and preterm birth can be detected earlier when artificial intelligence is used.

Artificial intelligence (AI)-aided disease prediction
Artificial Intelligence (AI)-aided Disease Prediction Announcing a new article publication for BIO Integration journal.

Artificial intelligence dives into thousands of WW2 photographs
In a new international cross disciplinary study, researchers from Aarhus University, Denmark and Tampere University, Finland have used artificial intelligence to analyse large amounts of historical photos from WW2.

Applying artificial intelligence to science education
A new review published in the Journal of Research in Science Teaching highlights the potential of machine learning--a subset of artificial intelligence--in science education.

New roles for clinicians in the age of artificial intelligence
New Roles for Clinicians in the Age of Artificial Intelligence Announcing a new article publication for BIO Integration journal.

Artificial intelligence aids gene activation discovery
Scientists have long known that human genes are activated through instructions delivered by the precise order of our DNA.

Artificial intelligence recognizes deteriorating photoreceptors
A software based on artificial intelligence (AI), which was developed by researchers at the Eye Clinic of the University Hospital Bonn, Stanford University and University of Utah, enables the precise assessment of the progression of geographic atrophy (GA), a disease of the light sensitive retina caused by age-related macular degeneration (AMD).

Classifying galaxies with artificial intelligence
Astronomers have applied artificial intelligence (AI) to ultra-wide field-of-view images of the distant Universe captured by the Subaru Telescope, and have achieved a very high accuracy for finding and classifying spiral galaxies in those images.

Read More: Artificial Intelligence News and Artificial Intelligence Current Events is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to