Publications by authors named "D Van Laere"
Article Synopsis
- Non-invasive cardiac output monitoring using electrical biosensing technology (EBT) allows for continuous monitoring of hemodynamic variables in neonates, helping to identify instability early for potential interventions.
- The use of thoracic (TEBT) and whole body (WBEBT) monitoring methods has grown in neonatology, although TEBT is not a reliable measure of cardiac output, it may track changes in individual patients over time.
- Recommendations suggest avoiding WBEBT for cardiac output monitoring and highlight the need for further research to address variations in technology and methodology before EBT can become routine in clinical practice.
Article Synopsis
- Developed an AI software system to predict late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) in premature infants in the NICU using continuous monitoring data.
- The study used an XGBoost machine learning algorithm on a dataset of 865 preterm infants, achieving a sensitivity of 69% for all episodes and 81% for severe cases, significantly reducing the time to diagnosis.
- The AI model's predictions can support clinicians' early detection efforts, indicating potential clinical and socioeconomic benefits, with further studies needed to understand the combined impact of AI and clinical expertise on patient outcomes.
Objective: To test the potential utility of applying machine learning methods to regional cerebral (rcSO) and peripheral oxygen saturation (SpO) signals to detect brain injury in extremely preterm infants.
Study Design: A subset of infants enrolled in the Management of Hypotension in Preterm infants (HIP) trial were analysed ( = 46). All eligible infants were <28 weeks' gestational age and had continuous rcSO measurements performed over the first 72 h and cranial ultrasounds performed during the first week after birth.
Application and potential of artificial intelligence in neonatal medicine.
Semin Fetal Neonatal Med
October 2022
Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies.
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