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ORIGINAL RESEARCH article

Front. Pediatr.

Sec. Neonatology

Volume 13 - 2025 | doi: 10.3389/fped.2025.1617155

Digital Biomarkers as predictors of brain injury in Neonatal Encephalopathy

Provisionally accepted
Nahla  ZaghloulNahla Zaghloul1Neel  Kamal SinghNeel Kamal Singh2Weihuang  XuWeihuang Xu1Kaitlyn  StewardKaitlyn Steward1Livia  SuraLivia Sura1Juan  Carlos RoigJuan Carlos Roig1Mehmet  AlbayramMehmet Albayram1Dhanashree  RajderkarDhanashree Rajderkar1James  Lawrence WynnJames Lawrence Wynn1Alina  ZareAlina Zare1Michael  D WeissMichael D Weiss1*
  • 1University of Florida, Gainesville, United States
  • 2Providence Sacred Heart Medical Center and Children's Hospital, Spokane, Washington, United States

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

Background: Neonatal encephalopathy (NE) is a significant cause of neurodevelopmental impairment, with therapeutic hypothermia (TH) being the current standard of care for mitigating brain injury in affected neonates. Despite advances, there is a critical need for early, reliable biomarkers that can predict brain injury severity and long-term outcomes, particularly during the 72-hour hypothermia window. This study explores the potential of digital biomarkers derived from continuous bedside physiologic monitoring to predict MRI confirmed brain injury in neonates with NE. Methods: We collected continuous physiologic data from 138 neonates undergoing TH, including heart rate, systemic oxygen saturation (SpO₂), cerebral oxygen saturation (rcSO₂), systolic and diastolic blood pressure, and mean arterial pressure (MAP). Using a Long Short-Term Memory (LSTM) neural network, we developed predictive models to classify neonates into no/mild or moderate/severe brain injury groups based on MRI findings. Model performance was evaluated at 24 and 48 hours of data collection. An ablation study was conducted to assess the relative importance of individual biomarkers. Results: Seventy-three neonates (52.9%) were classified as having moderate/severe injury, while 65 neonates (47.1%) had no/mild injury on MRI. The predictive accuracy of the LSTM model improved significantly with extended data duration, achieving an accuracy of 91.2% at 48 hours compared to 84.6% at 24 hours. The ablation study identified heart rate as the most significant biomarker, whereas rcSO₂ trends showed potential but did not consistently contribute to prediction accuracy in later models. Conclusion: 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.

Keywords: neonatal encephalopathy, Therapeutic hypothermia, digital biomarkers, Brain Injury, long short-term memory (LSTM) neural network, machine learning models

Received: 23 Apr 2025; Accepted: 31 Jul 2025.

Copyright: © 2025 Zaghloul, Singh, Xu, Steward, Sura, Roig, Albayram, Rajderkar, Wynn, Zare and Weiss. 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: Michael D Weiss, University of Florida, Gainesville, United States

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