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

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dc.contributor.authorTuncay, Suat
dc.contributor.authorOzden, Gurkan
dc.contributor.authorSarman, Abdullah
dc.contributor.authorGuducu Tufekci, Fatma
dc.date.accessioned2026-04-04T13:33:27Z
dc.date.available2026-04-04T13:33:27Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractBackgroundConjugated 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.
dc.identifier.doi10.1097/DCC.0000000000000729
dc.identifier.endpageE7
dc.identifier.issn0730-4625
dc.identifier.issn1538-8646
dc.identifier.issue6
dc.identifier.orcid0000-0002-5081-4593
dc.identifier.orcid0000-0001-5493-6507
dc.identifier.orcid0000-0002-2775-3163
dc.identifier.startpageE1
dc.identifier.urihttps://doi.org/10.1097/DCC.0000000000000729
dc.identifier.urihttps://hdl.handle.net/11616/109175
dc.identifier.volume44
dc.identifier.wosWOS:001587318400008
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofDimensions of Critical Care Nursing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectArtificial intelligence
dc.subjectComputer
dc.subjectJaundice
dc.subjectNewborn
dc.subjectNursing
dc.titleComputer Vision-Based Artificial Intelligence Tool for Direct Bilirubin Jaundice Measurement in Newborns
dc.typeArticle

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