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AGMA-PESS: a deep learning-based infant pose estimator and sequence selector software for general movement assessment
Ameur Soualmi1,2, Olivier Alata1, Christophe Ducottet1
1Laboratoire Hubert Curien UMR 5516, CNRS, Institut d'Optique Graduate School Université Jean Monnet Saint-Etienne, Saint-Etienne, France.
Insights
Automating the General Movement Assessment (GMA) for preterm infants using the AGMA-PESS software simplifies video analysis. This tool enhances early brain maturation evaluation in neonatal units for better developmental outcomes.
Area of Science:
- Neonatal Neuroscience
- Developmental Pediatrics
- Medical Imaging Analysis
Background:
- The General Movement Assessment (GMA) is crucial for evaluating brain maturation in preterm infants.
- Current GMA requires lengthy video recordings and manual selection of movement sequences, hindering clinical use.
- Accurate infant pose estimation is vital for automating GMA.
Purpose of the Study:
- To introduce the AGMA Pose Estimator and Sequence Selector (AGMA-PESS) software.
- To automate the selection of relevant video sequences for GMA.
- To enable 2D pose estimation of preterm infants for GMA.
Main Methods:
- Utilized a state-of-the-art deep learning infant pose estimation network.
- Developed software (AGMA-PESS) for automatic sequence selection and 2D pose estimation.
- Tested on preterm infants at preterm and writhing ages.
Main Results:
- AGMA-PESS successfully automates the selection of video sequences for GMA.
- The software provides accurate 2D pose estimation for infants.
- Demonstrated simplicity and efficiency in processing video data.
Conclusions:
- AGMA-PESS significantly reduces the time and effort required for GMA analysis.
- The software facilitates wider implementation of GMA in Neonatal Units.
- AGMA-PESS supports both clinical practice and research in infant development.
Abstract:
The General Movement Assessment (GMA) is a validated evaluation of brain maturation essential to shaping early individual developmental trajectories of preterm infants. To ensure a reliable GMA, preterm infants should be recorded for 30 to 60 min before manually selecting at least three sequences with general movements. This time-consuming task of manually selecting short video sequences from lengthy recordings impedes its implementation within the Neonatal Unit. Moreover, an accurate pose estimation tool for preterm infants is paramount to developing the field of GMA automation. We introduce the AGMA Pose Estimator and Sequence Selector (AGMA-PESS) software, based on the state-of-the-art deep learning infant pose estimation network, to automatically select the video sequences for GMA at preterm and writhing ages and estimate the pose of infants in 2D. Its simplicity and efficiency make AGMA-PESS a valuable tool to promote GMA use within the Neonatal Unit, both for clinical practice and research purposes.
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