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TECHNOLOGY AND CODE article

Front. Pediatr.
Sec. Neonatology
Volume 12 - 2024 | doi: 10.3389/fped.2024.1465632
This article is part of the Research Topic Artificial Intelligence and Machine Learning in Pediatrics View all 3 articles

AGMA-PESS: a deep learning-based infant pose estimator and sequence selector software for General Movement assessment

Provisionally accepted
Ameur Soualmi Ameur Soualmi 1,2Olivier Alata Olivier Alata 2Christophe Ducottet Christophe Ducottet 2Anne Petitjean-Robert Anne Petitjean-Robert 3Aurélie Plat Aurélie Plat 4Hugues Patural Hugues Patural 1,4Antoine Giraud Antoine Giraud 1,4*
  • 1 INSERM U1059 SAnté INgéniérie BIOlogie, Saint-Etienne, France
  • 2 Universit ́e Jean Monnet Saint-Etienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023., Saint-Étienne, France
  • 3 Service de Néonatalogie, Centre Hospitalier Universitaire de Saint-Étienne, Saint-Étienne, France
  • 4 Service de Réanimation Néonatale, Centre Hospitalier Universitaire de Saint-Étienne, Saint-Étienne, France

The final, formatted version of the article will be published soon.

    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 minutes 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.

    Keywords: preterm infant pose estimation, automatic sequence selection, General movements, Windows software, infant spontaneous movements

    Received: 16 Jul 2024; Accepted: 03 Dec 2024.

    Copyright: © 2024 Soualmi, Alata, Ducottet, Petitjean-Robert, Plat, Patural and Giraud. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Antoine Giraud, INSERM U1059 SAnté INgéniérie BIOlogie, Saint-Etienne, France

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.