Digital biomarkers as predictors of brain injury in neonatal encephalopathy
Our study highlights the potential of digital biomarkers in predicting brain injury severity during the therapeutic hypothermia window. Machine learning models, such as LSTM networks, offer an opportunity for real-time prediction and risk stratification, ultimately enhancing clinical decision-making and neuroprotective strategies in neonates with NE. Future studies will focus on integrating real-time data capture and improving predictive accuracy.
Authors
- James L. Wynn (ORCID: https://orcid.org/0000-0001-7052-4836)
- Dhanashree Rajderkar (ORCID: https://orcid.org/0000-0002-5962-2854)
- Livia Sura
- Sait Albayram (ORCID: https://orcid.org/0000-0001-9111-6815)
- Nahla Zaghloul (ORCID: https://orcid.org/0000-0001-7172-7395)
- Michael D. Weiss (ORCID: https://orcid.org/0000-0003-4064-1032)
- Alina Zare (ORCID: https://orcid.org/0000-0002-4847-7604)
- Weifeng Xu (ORCID: https://orcid.org/0000-0001-6682-3075)
- Nalini Singh
- Josep Roig
- Kaitlyn Lagnese
Institutions
- University of Florida (US)
Publication Details
- Journal
- Frontiers in Pediatrics
- Published
- 2025-08-29
- DOI
- https://doi.org/10.3389/fped.2025.1617155
- Primary Topic
- Neonatal and fetal brain pathology
- Type
- article
- Field-Weighted Citation Impact
- 0.00