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Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies
Chiara Tacchino1, Martina Impagliazzo2, Erika Maggi2
1Physical Medicine and Rehabilitation, Gaslini Pediatric Hospital, Genoa, Italy.
Insights
A new Markerless Infant Movement Analysis System (MIMAS) analyzes infant movements to predict neuro-motor deficits in preterm babies. This system, integrated with a biobank, shows promise for early diagnosis and improved care.
Area of Science:
- Biomedical Engineering
- Neonatology
- Developmental Pediatrics
Background:
- Globally, preterm birth rates are rising, increasing neonates' risk of neuro-motor and cognitive deficits.
- Current methods for assessing infant movement risk are time-consuming and not suitable for routine clinical practice.
- There is a need for accessible diagnostic tools to support preterm infants.
Purpose of the Study:
- To introduce the Markerless Infant Movement Analysis System (MIMAS) for analyzing spontaneous infant movements.
- To integrate MIMAS with a Biobank for diagnostic, prognostic, and epidemiological purposes in preterm infants.
- To develop a simple, low-cost computerized video analysis system for early risk detection.
Main Methods:
- MIMAS utilizes a single RGB camera for markerless video analysis of newborns in their natural environment.
- Videos are processed into binarized silhouettes to enhance robustness and minimize illumination effects.
- A comprehensive set of 39 parameters analyzes spatial and spectral silhouette changes, with data stored in a Biobank.
Main Results:
- The system effectively captures maturation in both preterm and full-term infants between birth and 8-12 weeks.
- MIMAS differentiates between preterm and full-term infants at birth, with differences diminishing by 8-12 weeks.
- The analysis demonstrated potential in identifying 'abnormal' preterm infants within the study cohort.
Conclusions:
- Preliminary findings support MIMAS as a valuable clinical tool for analyzing infant movements.
- System adoption facilitates systematic data accumulation in the Biobank, enhancing analysis accuracy.
- The integration of MIMAS and the Biobank paves the way for advanced data mining techniques in neonatology.
Background And Objectives:
The number of preterm babies is steadily growing world-wide and these neonates are at risk of neuro-motor-cognitive deficits. The observation of spontaneous movements in the first three months of age is known to predict such risk. However, the analysis by specifically trained physiotherapists is not suited for the clinical routine, motivating the development of simple computerized video analysis systems, integrated with a well-structured Biobank to make available for preterm babies a growing service with diagnostic, prognostic and epidemiological purposes.
Methods:
MIMAS (Markerless Infant Movement Analysis System) is a simple, low-cost system of video analysis of spontaneous movements of newborns in their natural environment, based on a single standard RGB camera, without markers attached to the body. The original videos are transformed into binarized sequences highlighting the silhouette of the baby, in order to minimize the illumination effects and increase the robustness of the analysis; such sequences are then coded by a large set of parameters (39) related to the spatial and spectral changes of the silhouette. The parameter vectors of each baby were stored in the Biobank together with related clinical information.
Results:
The preliminary test of the system was carried out at the Gaslini Pediatric Hospital in Genoa, where 46 preterm (PT) and 21 full-term (FT) babies (as controls) were recorded at birth (T0) and 8-12 weeks thereafter (T1). A simple statistical analysis of the data showed that the coded parameters are sensitive to the degree of maturation of the newborns (comparing T0 with T1, for both PT and FT babies), and to the conditions at birth (PT vs. FT at T0), whereas this difference tends to vanish at T1. Moreover, the coding method seems also able to detect the few 'abnormal' preterm babies in the PT populations that were analyzed as specific case studies.
Conclusions:
Preliminary results motivate the adoption of this tool in clinical practice allowing for a systematic accumulation of cases in the Biobank, thus for improving the accuracy of data analysis performed by MIMAS and ultimately allowing the adoption of data mining techniques.
