A deep learning model for automated assessment of neonates at risk of impaired breast crawl: a proof-of-concept video-based study
- DOI
- 10.3389/fpubh.2026.1908206
- Published
- 2026-10-23
- Container
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fpubh.2026.1908206,
title = {A deep learning model for automated assessment of neonates at risk of impaired breast crawl: a proof-of-concept video-based study},
author = {Zhu Xiang and Yunxuan Tong and Yuqi Chen and Anqi Xiong and Xinyu Shi and Biru Luo},
year = {2026},
journal = {Frontiers in Public Health},
doi = {10.3389/fpubh.2026.1908206},
url = {https://doi.org/10.3389/fpubh.2026.1908206}
}RIS
TY - JOUR TI - A deep learning model for automated assessment of neonates at risk of impaired breast crawl: a proof-of-concept video-based study AU - Zhu Xiang AU - Yunxuan Tong AU - Yuqi Chen AU - Anqi Xiong AU - Xinyu Shi AU - Biru Luo PY - 2026 JO - Frontiers in Public Health DO - 10.3389/fpubh.2026.1908206 UR - https://doi.org/10.3389/fpubh.2026.1908206 ER -
APA
Xiang, Z., Tong, Y., Chen, Y., Xiong, A., Shi, X., & Luo, B. (2026). A deep learning model for automated assessment of neonates at risk of impaired breast crawl: a proof-of-concept video-based study. Frontiers in Public Health. https://doi.org/10.3389/fpubh.2026.1908206
Source records
- crossref · retrieved 2026-11-09T15:09:42.847Z