AI-augmented prenatal care: a dual-modal fetal health assessment system integrating cardiotocography and uterine contraction synergy
This study achieves a balance between clinical interpretability and high performance through lightweight AI design (only 8.3 million parameters) and dual-modality data fusion, making it particularly suitable for resource-constrained primary care settings. Future work should further optimize generalization capabilities through multicenter validation and explore integration with large language models to generate standardized reports. These findings provide important references for optimizing perinatal healthcare resources and AI-assisted decision-making.
Authors
- Shiling Jiang
- Qingshan You (ORCID: https://orcid.org/0000-0002-1806-1560)
- T. Qiu (ORCID: https://orcid.org/0000-0002-8369-6626)
- Xiangqin Zhou (ORCID: https://orcid.org/0000-0002-6434-4764)
- Jun Zhou (ORCID: https://orcid.org/0000-0001-5822-8233)
- Hui Cheng (ORCID: https://orcid.org/0000-0003-2579-7004)
- Chunxia Lin (ORCID: https://orcid.org/0009-0002-2481-8344)
- Xinhao Wang (ORCID: https://orcid.org/0009-0002-5639-9914)
Institutions
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- Civil Aviation Flight University of China (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-09-29
- DOI
- https://doi.org/10.3389/fphys.2025.1638788
- Citations
- 1
- Primary Topic
- Neonatal and fetal brain pathology
- Type
- article
- Field-Weighted Citation Impact
- 3.49