ClinicalTrials.gov
public full text
Public Domain
Role of Inflammatory Markers and Doppler Parameters in Late-Onset Fetal Growth Restriction: A Machine Learning Approach
Fetal growth restriction (FGR) is a serious complication in pregnancy that can lead to various adverse outcomes. It's classified into early-onset (before 32 weeks) and late-onset (after 32 weeks), with late-onset associated with long-term risks like hypoxemia and developmental delays. The study focuses on the role of inflammation in FGR, introducing new blood markers for better understanding and diagnosis. It also addresses the challenges of using advanced diagnostic tools in low-resource settings and explores the use of machine learning to predict FGR based on inflammatory markers, highlighting the potential of artificial intelligence in overcoming these challenges.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A machine learning system enables just-in-time risk-stratified sepsis evaluations in the neonatal intensive care unit.#301603
- Beyond the 'Pregnancy Black Box': a global roadmap for artificial intelligence-driven pharmacogenomics in maternal-neonatal health.#308794
- Development and external evaluation of an interpretable machine-learning model for early prediction of organ failure in higher-risk acute pancreatitis patients: A multicentre cohort study#1054733
- Artificial Intelligence for Neonatal Sepsis Prediction: A Review of Advances, Clinical Integration, and Future Prospects.#342535
- Transcriptomic Profiling in Severely Injured Patients#1087957
Record · ID 1282611
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive