An Exploratory Review on the Potential of Artificial Intelligence for Early Detection of Acute Kidney Injury in Preterm Neonates

Kandasamy, Yogavijayan, and Baker, Stephanie (2023) An Exploratory Review on the Potential of Artificial Intelligence for Early Detection of Acute Kidney Injury in Preterm Neonates. Diagnostics, 13 (18). 2865.

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A preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 15 million babies are born preterm annually worldwide, indicating a global preterm birth rate of about 11%. Up to 50% of premature neonates in the gestational age (GA) group of <29 weeks’ gestation will develop acute kidney injury (AKI) in the neonatal period; this is associated with high mortality and morbidity. There are currently no proven treatments for established AKI, and no effective predictive tool exists. We propose that the development of advanced artificial intelligence algorithms with neural networks can assist clinicians in accurately predicting AKI. Clinicians can use pathology investigations in combination with the non-invasive monitoring of renal tissue oxygenation (rSO2) and renal fractional tissue oxygenation extraction (rFTOE) using near-infrared spectroscopy (NIRS) and the renal resistive index (RRI) to develop an effective prediction algorithm. This algorithm would potentially create a therapeutic window during which the treating clinicians can identify modifiable risk factors and implement the necessary steps to prevent the onset and reduce the duration of AKI.

Item ID: 80269
Item Type: Article (Research - C1)
ISSN: 2075-4418
Copyright Information: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Funders: National Health and Medical Research Council (NHMRC)
Projects and Grants: NHMRC APP1159616
Date Deposited: 06 Sep 2023 03:40
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3213 Paediatrics > 321303 Neonatology @ 33%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320214 Nephrology and urology @ 33%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 34%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 33%
20 HEALTH > 2005 Specific population health (excl. Indigenous health) > 200506 Neonatal and child health @ 33%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 34%
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