ArticleFrontiers in physiology2022
Early Prediction of Hemodynamic Shock in Pediatric Intensive Care Units With Deep Learning on Thermal Videos.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Uses of infrared thermography in acute illness: a systematic review.Frontiers in medicine · 2024Pooled it
- Creating an engine of scientific discovery, an Indo-Japan perspective: learnings from IJNTC 2025 workshop.NPJ systems biology and applications · 2026Review
- ContraIndicator: A Natural Language Processing-Based Approach to Potential Drug-Drug Interaction Detection in Pediatric Intensive Care.Clinical drug investigation · 2026Observational
- Core temperature measurement methods and their accuracy in pediatric intensive care units: a narrative review.Frontiers in pediatrics · 2026Review
- Application of Infrared Thermography in Early Screening of Sepsis and Prediction of Septic Shock Risk: A Systematic Review.Emergency medicine international · 2026Review
- Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Shock is one of the major killers in intensive care units, and early interventions can potentially reverse it. In this study, we advance a noncontact thermal imaging modality for continuous monitoring of hemodynamic shock working on 1,03,936 frames from 406 videos recorded longitudinally upon 22 pediatric patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with the heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 h. Our models achieved the best area under the receiver operating characteristic curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 h, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline that can provide better care and save lives.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.