Computer Vision-Based Artificial Intelligence Tool for Direct Bilirubin Jaundice Measurement in Newborns

BackgroundConjugated hyperbilirubinemia, characterized by elevated levels of direct bilirubin (DB) may indicate underlying hepatobiliary disorders, such as biliary atresia, and warrants further investigation.ObjectivesThe aim of this study was to accurately measure jaundice, related to DB levels, in newborn infants using an artificial intelligence (AI)-based computer vision tool.MethodThe computer vision tool used data processing, color transformations, and contrast enhancement techniques. Additionally, a convolutional neural network was created to predict DB levels. The study included 80 infants for training and validation and 17 infants for retesting. Five photographs were taken from the face, neck-chest, abdomen, extremities, and back after blood was drawn to measure DB levels. The photographs were captured using a professional camera under white light in the neonatal intensive care unit. Data analysis involved calculating the margin of error, percentage margin of error, and correlation statistics.ResultsThe retest findings were analyzed to determine the margins of error. The study revealed a 5.24% discrepancy between the AI-based computer vision tool and the DB values obtained from laboratory blood tests. Furthermore, a positive correlation was observed between patient blood values and the mean calculated by the AI system.DiscussionThis study concluded that DB measurements, conducted under appropriate conditions, were accurately determined using AI with a good level of precision. Subsequent research on total bilirubin is recommended.

Dergi

DIMENSIONS OF CRITICAL CARE NURSING

Yıl

2025

Cilt

44

Sayı

6

Sayfa

E1-E7

ISSN

0730-4625

eISSN

1538-8646

DOI

10.1097/DCC.0000000000000729

WoS UID

WOS:001587318400008

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