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Recognizing Kidney Injury Due to Burns Is Improved by AI

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Acute kidney injury usually happens within the first week of a serious burn.

Researchers at the University of California, Davis have developed an Artificial Intelligence/Machine Learning model that can predict acute kidney injury resulting from burns quicker and more accurately than ever.


Researchers at the University of California, Davis (UC Davis) have developed an artificial intelligence (AI)/machine learning (ML) model that can predict burn-related acute kidney injury (AKI) quicker and more accurately than ever.

UC Davis researchers identified the role of a new biomarker for AKI diagnosis, neutrophil gelatinase associated lipocalin (NGAL).

Models were trained and tested with clinical laboratory data for 50 adult burn patients that had NGAL, urine output, creatinine, and NT-proBNP measured within the first 24 hours of admission. The models containing NGAL, creatinine, urine output, and NT-proBNP achieved 90–100% accuracy in identifying AKI; models lacking NGAL achieved 80–90% accuracy.

Said UC Davis' Hooman Rashidi, "We envision such machine learning platforms to be incorporated in a variety of settings outside of AKI which could ultimately enhance various aspects of patient care within the clinical medicine arena."

From UC Davis Health Newsroom
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Abstracts Copyright © 2019 SmithBucklin, Washington, DC, USA


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