AIMC Topic: Blood Specimen Collection

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A Near-Infrared Imaging System for Robotic Venous Blood Collection.

Sensors (Basel, Switzerland) Nov 20, 2024
Venous blood collection is a widely used medical diagnostic technique, and with rapid advancements in robotics, robotic venous blood collection has the potential to replace traditional manual methods. The success of this robotic approach is heavily d...
Veins Blood Specimen Collection Algorithms Neural Networks, Computer Robotics Image Processing, Computer-Assisted Humans
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Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

Journal of clinical pathology Jun 29, 2019
AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagn...
Blood Specimen Collection Cell Lineage Pattern Recognition, Automated Leukemia Acute Disease Predictive Value of Tests Image Interpretation, Computer-Assisted Leukocytes Staining and Labeling Diagnosis, Differential Machine Learning Reproducibility of Results Humans
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Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach.

Journal of medical systems Oct 26, 2017
Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed health...
Pattern Recognition, Automated Telemedicine Blood Specimen Collection Malaria Bayes Theorem Machine Learning Internet Algorithms Humans Image Processing, Computer-Assisted
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An updated method for the jugular catheterization of grower pigs for repeated blood sampling following an oral glucose tolerance test.

Laboratory animals Jan 1, 2016
Jugular catheterization is a common procedure used under experimental conditions. However, there is considerable variation in the reported techniques, particularly for grower pigs (>40 kg and <60 kg) when larger volumes of blood per sample (>10 mL) a...
Jugular Veins Catheterization, Central Venous Phlebotomy Animals Blood Specimen Collection Catheterization Humans Glucose Tolerance Test Swine
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Measuring the operational performance of an artificial intelligence-based blood tube-labeling robot, NESLI.

American journal of clinical pathology Feb 12, 2025
OBJECTIVES: Laboratory testing, crucial for medical diagnosis, has 3 phases: preanalytical, analytical, and postanalytical. This study set out to demonstrate whether automating tube labeling through artificial intelligence (AI) support enhances effic...
Humans Blood Specimen Collection Phlebotomy Robotics Artificial Intelligence
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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.

American journal of clinical pathology Jun 3, 2024
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 preanal...
Artificial Intelligence Robotics Humans Blood Specimen Collection Quality Control
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Using Machine Learning-Based Multianalyte Delta Checks to Detect Wrong Blood in Tube Errors.

American journal of clinical pathology Oct 24, 2018
OBJECTIVES: An unfortunate reality of laboratory medicine is that blood specimens collected from one patient occasionally get mislabeled with identifiers from a different patient, resulting in so-called "wrong blood in tube" (WBIT) errors and potenti...
Humans Medical Errors Algorithms Blood Specimen Collection Patient Safety Machine Learning
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