AGMA-PESS: Software for Infant Movement Assessment
Author Information
Author(s): Soualmi Ameur, Alata Olivier, Ducottet Christophe, Petitjean-Robert Anne, Plat Aurélie, Patural Hugues, Giraud Antoine
Primary Institution: Institut d’Optique Graduate School Université Jean Monnet Saint-Etienne
Hypothesis
Can deep learning improve the efficiency of general movement assessments in preterm infants?
Conclusion
The AGMA-PESS software effectively automates the selection of video sequences for general movement assessment, achieving high precision comparable to human experts.
Supporting Evidence
- The AGMA-PESS software achieved a precision of 86% in selecting video sequences.
- In 28 out of 30 cases, the software's selections intersected with those of human experts.
- The software was developed using a dataset of over 88,000 images of infants.
- Manual selection of sequences took an average of over 23 minutes, while the software automates this process.
Takeaway
This software helps doctors quickly find important videos of baby movements, making it easier to check if they are developing well.
Methodology
The software uses deep learning to estimate infant poses and automatically select video sequences for general movement assessment.
Limitations
The software may not generalize well outside the specific conditions of the study and cannot differentiate between movements caused by crying or fussing.
Participant Demographics
Six preterm infants born before 33 weeks of gestational age were included.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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