Measuring the performance of an artificial intelligence-based robot that classifies blood tubes and performs quality control in terms of preanalytical errors: A preliminary study.

Autor:	Şişman AR; Department of Medical Biochemistry, School of Medicine, Dokuz Eylul University, Inciralti, Izmir, Turkey., Başok Bİ; Izmir Faculty of Medicine, Department of Medical Biochemistry, University of Health Sciences Turkey, Konak, Izmir, Turkey.; Clinical Chemistry Laboratory, Dr Behcet Uz Children Health and Surgery Education and Research Hospital, University of Health Sciences Turkey, Yenişehir, Izmir, Turkey., Karakoyun İ; Izmir Faculty of Medicine, Department of Medical Biochemistry, University of Health Sciences Turkey, Yenişehir, Izmir, Turkey., Çolak A; Izmir Faculty of Medicine, Department of Medical Biochemistry, University of Health Sciences Turkey, Yenişehir, Izmir, Turkey., Bilge U; Faculty of Medicine, Department of Biostatistics and Medical Informatics, Akdeniz University, Antalya, Turkey., Demirci F; Izmir Faculty of Medicine, Department of Medical Biochemistry, University of Health Sciences Turkey, Yenişehir, Izmir, Turkey., Başoglu N; Izmir Institute of Technology, Urla, Izmir, Turkey.
Jazyk:	angličtina
Zdroj:	American journal of clinical pathology [Am J Clin Pathol] 2024 Jun 03; Vol. 161 (6), pp. 553-560.
DOI:	10.1093/ajcp/aqad179
Abstrakt:	Objectives: Artificial intelligence-based robotic systems are increasingly used in medical laboratories. This study aimed to test the performance of KANKA (Labenko), a stand-alone, artificial intelligence-based robot that performs sorting and preanalytical quality control of blood tubes. Methods: KANKA is designed to perform preanalytical quality control with respect to error control and preanalytical sorting of blood tubes. To detect sorting errors and preanalytical inappropriateness within the routine work of the laboratory, a total of 1000 blood tubes were presented to the KANKA robot in 7 scenarios. These scenarios encompassed various days and runs, with 5 repetitions each, resulting in a total of 5000 instances of sorting and detection of preanalytical errors. As the gold standard, 2 experts working in the same laboratory identified and recorded the correct sorting and preanalytical errors. The success rate of KANKA was calculated for both the accurate tubes and those tubes with inappropriate identification. Results: KANKA achieved an overall accuracy rate of 99.98% and 100% in detecting tubes with preanalytical errors. It was found that KANKA can perform the control and sorting of 311 blood tubes per hour in terms of preanalytical errors. Conclusions: KANKA categorizes and records problem-free tubes according to laboratory subunits while identifying and classifying tubes with preanalytical inappropriateness into the correct error sections. As a blood acceptance and tube sorting system, KANKA has the potential to save labor and enhance the quality of the preanalytical process. (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
Databáze:	MEDLINE
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