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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.09821 |
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| _version_ | 1866916884540030976 |
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| author | Chopard, Daphné Laguna, Sonia Chin-Cheong, Kieran Dietz, Annika Badura, Anna Wellmann, Sven Vogt, Julia E. |
| author_facet | Chopard, Daphné Laguna, Sonia Chin-Cheong, Kieran Dietz, Annika Badura, Anna Wellmann, Sven Vogt, Julia E. |
| contents | General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are limited in number. To scale up newborn screening, there is a need for an algorithm that can automatically classify GMs from infant video recordings. This data poses challenges, including variability in recording length, device type, and setting, with each video coarsely annotated for overall movement quality. In this work, we introduce a tool for extracting features from these recordings and explore various machine learning techniques for automated GM classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09821 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings Chopard, Daphné Laguna, Sonia Chin-Cheong, Kieran Dietz, Annika Badura, Anna Wellmann, Sven Vogt, Julia E. Machine Learning Computer Vision and Pattern Recognition General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are limited in number. To scale up newborn screening, there is a need for an algorithm that can automatically classify GMs from infant video recordings. This data poses challenges, including variability in recording length, device type, and setting, with each video coarsely annotated for overall movement quality. In this work, we introduce a tool for extracting features from these recordings and explore various machine learning techniques for automated GM classification. |
| title | Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.09821 |