Predicting Hemodynamic Shock from Thermal Images using Machine Learning

Proactive detection of hemodynamic shock can prevent organ failure and save lives. Thermal imaging is a non-invasive, non-contact modality to capture body surface temperature with the potential to reveal underlying perfusion disturbance in shock. In this study, we automate early detection and predic...

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Bibliographic Details
Published in:Scientific reports 2019-01, Vol.9 (1), p.91-91, Article 91
Main Authors: Nagori, Aditya, Dhingra, Lovedeep Singh, Bhatnagar, Ambika, Lodha, Rakesh, Sethi, Tavpritesh
Format: Article
Language:English
Subjects:
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Adolescent
Artificial intelligence
Blood Pressure
Body temperature
Child
Child, Preschool
Dengue fever
Heart Rate
Humanities and Social Sciences
Humans
Image processing
Infant
Learning algorithms
Longitudinal Studies
Machine Learning
Male
Models, Statistical
multidisciplinary
Optical Imaging - methods
Perfusion
Science
Science (multidisciplinary)
Segmentation
Shock
Shock, Cardiogenic - diagnosis
Shock, Cardiogenic - pathology
Surface temperature
Thermometry - methods
Vector-borne diseases
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Summary:Proactive detection of hemodynamic shock can prevent organ failure and save lives. Thermal imaging is a non-invasive, non-contact modality to capture body surface temperature with the potential to reveal underlying perfusion disturbance in shock. In this study, we automate early detection and prediction of shock using machine learning upon thermal images obtained in a pediatric intensive care unit of a tertiary care hospital. 539 images were recorded out of which 253 had concomitant measurement of continuous intra-arterial blood pressure, the gold standard for shock monitoring. Histogram of oriented gradient features were used for machine learning based region-of-interest segmentation that achieved 96% agreement with a human expert. The segmented center-to-periphery difference along with pulse rate was used in longitudinal prediction of shock at 0, 3, 6 and 12 hours using a generalized linear mixed-effects model. The model achieved a mean area under the receiver operating characteristic curve of 75% at 0 hours (classification), 77% at 3 hours (prediction) and 69% at 12 hours (prediction) respectively. Since hemodynamic shock associated with critical illness and infectious epidemics such as Dengue is often fatal, our model demonstrates an affordable, non-invasive, non-contact and tele-diagnostic decision support system for its reliable detection and prediction.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-36586-8